Two 48 GB workstation cards, one 70B model, and a bandwidth advantage that never reaches the token counter.
The Radeon Pro W7900 has more bandwidth, yet the RTX A6000 runs 70B Q4 inference about 29% faster (14.60 vs 11.35 tok/s). One clear verdict.
Dense 70B+ models need about 42.5 GB for their Q4 weights; on the RTX A6000 48GB, the weights and a usable context window both fit. SpecPicks tracks 9 community runs of that size on this card, median 14.6 tok/s. RTX A6000 48GB carries 48 GB of VRAM. The weights column is the range of real Q4_K_M files on Hugging Face for the models in each size class (about 0.6 GB per billion parameters), and the runtime plus a usable context window wants about 2 GB on top; every tokens-per-second figure is a median over community-reported Q4 runs SpecPicks tracks for this card, with the run count and the source beside it.
Showing the model sizes this article covers and the band either side. Every size from 3B to 70B+, for every card SpecPicks tracks, is in the local-LLM GPU table.
| Model size | Weights at Q4 | Fits in 48 GB? | Measured | Left for context | Source |
|---|---|---|---|---|---|
| 30-35B (Qwen 3 32B, QwQ 32B)The step change. A 24 GB card holds this entirely in VRAM; below that it is CPU offload. | about 19.9 GB | Fits: weights and a usable context window | 26.2 tok/s6 runs · 3 sources | ~28 GBfor runtime and KV cache | DatabaseMart |
| 70B+ (Llama 3.3 70B, Qwen 2.5 72B)One 48 GB card or two 24 GB cards. A 32 GB card runs it only with layers in system RAM. | about 42.5 GB | Fits: weights and a usable context window | 14.6 tok/s9 runs · 4 sources | ~5 GBfor runtime and KV cache | DatabaseMart |
Every RTX A6000 48GB benchmark run, with its source → GPU picks for local LLM — the same table across every card we track How we source these numbers
Forty-eight gigabytes is a threshold, not a spec. It is the smallest single-card frame buffer that holds a 70B-class dense model at four-bit quantization with a usable context window, and everything below it forces you into either a smaller model or a multi-GPU configuration with the wiring, power and tensor-parallel bookkeeping that implies. Two cards realistically serve that threshold for someone who is not buying a rack: AMD's Radeon Pro W7900 and NVIDIA's RTX A6000.
They arrived from different directions. The A6000 launched in 2020 as an Ampere workstation part at a $4,650 list price, and years of enterprise refresh cycles have pushed a steady supply into the second-hand market. The W7900 arrived in 2023 as RDNA 3 silicon at $3,999 list, still sold new, with 96 GB/s more memory bandwidth and a newer architecture behind it. On the specification sheet the Radeon is the modern card.
On measured 70B inference it is not the faster one, and the gap is consistent across independent test sources. That result deserves an explanation rather than a shrug, because it is entirely reproducible and it tells you something about what you are actually buying at this tier: not a bandwidth figure, but a software ecosystem.
This synthesis is for the builder deciding where a four-figure GPU budget goes on a machine that will hold a 70B model resident. It covers what fits, how fast each card runs it, what the software stack costs in engineering time, and the case for skipping this tier entirely.
Before either card is named, settle the topology question, because it changes the budget by a factor of two.
One 48 GB card gives you a single memory pool. A 70B model at Q4_K_M loads end to end with no sharding configuration, no tensor-parallel launch flags, and no model that refuses to split cleanly across an odd layer count. One PCIe slot, one power connector set, one thermal problem. On a machine you intend to leave running, that simplicity has real operational value.
Two 24 GB cards usually deliver more raw throughput per dollar, because consumer silicon is clocked far more aggressively than workstation parts. The costs are practical: roughly double the board power, two slots plus airflow between them, a tensor-parallel or layer-split configuration to maintain across runtime upgrades, and PCIe bandwidth that becomes a real limit if the split is layer-wise rather than tensor-wise.
Neither, if the model is smaller than you think. A 32B model at Q4_K_M fits comfortably on 24 GB — DatabaseMart measures Qwen2.5 32B Q4_K_M at 26.08 tok/s in 32.2 GB on the A6000, and the same model at four-bit is a 24 GB workload. If a 32B model does your job, this entire tier is optional. The SpecPicks 24 GB GPU guide covers that decision.
Answer Step 0 honestly. Most people asking "which 48 GB card" are actually asking "how do I run a 70B model," and the two questions have different answers.
| Spec | Radeon Pro W7900 48GB | RTX A6000 48GB | Why it matters for inference | Source |
|---|---|---|---|---|
| VRAM | 48 GB GDDR6 | 48 GB GDDR6 with ECC | Identical ceiling; both hold 70B at Q4 | TechPowerUp |
| Memory bus | 384-bit | 384-bit | Same width | TechPowerUp |
| Memory bandwidth | 864.0 GB/s | 768.0 GB/s | Generation is memory-bound — Radeon leads on paper | TechPowerUp |
| Shading units | 6,144 | 10,752 CUDA cores | Prefill is compute-bound; NVIDIA leads heavily | TechPowerUp |
| Board power | 295 W | 300 W | Effectively identical thermal budget | hardware_specs / TechPowerUp |
| Architecture | RDNA 3 (Navi 31) | Ampere (GA102) | Newer silicon on the AMD side | TechPowerUp |
| Launch year / list | 2023 / $3,999 | 2020 / $4,650 | A6000 is the used-market card | AMD / NVIDIA |
| PassMark G3D | 27,729 | 22,767 | Rasterisation lead does not transfer to inference | PassMark |
| 3DMark Time Spy | 15,787 | 17,742 | Synthetics disagree with each other | TopCPU |
| Primary runtime | ROCm | CUDA | Decides how much bandwidth you actually use | AMD / NVIDIA |
The interesting row is PassMark against inference. PassMark rates the W7900 at 27,729 G3D points versus 22,767 for the A6000 — a 22% Radeon lead — while TopCPU's 3DMark Time Spy aggregation puts them the other way round at 15,787 to 17,742. Neither synthetic predicts the inference ordering. Full specifications are on AMD's Radeon Pro W7900 page, NVIDIA's RTX A6000 page, and TechPowerUp's database entries for the W7900 and the A6000.
A 70B-class dense model is the design point. The table below derives weight footprints from a 70B parameter count at each GGUF scheme's nominal bits-per-weight, alongside the VRAM figures actually measured on these cards.
| Quant | Approx. bits/weight | Derived weight size | Measured VRAM at 48 GB | Verdict |
|---|---|---|---|---|
| Q2_K | ~3.0 | ~26 GB | — | Fits with large context; quality loss is severe on 70B reasoning |
| Q3_K_M | ~3.9 | ~34 GB | — | Fits with room for long context |
| Q4_0 | ~4.5 | ~39 GB | 39.0 GB (llm-tracker, W7900) | Fits; the ExLlamaV2 configuration |
| Q4_K_M | ~4.85 | ~42 GB | 42.5-46.0 GB (both cards) | The 48 GB design point — fits with modest context |
| Q5_K_M | ~5.7 | ~50 GB | — | Over the ceiling; offload or a second card |
| Q6_K | ~6.6 | ~58 GB | — | Two-card territory |
| Q8_0 | ~8.5 | ~74 GB | — | Two cards minimum |
The measured column is the one to trust. OpenLLM Benchmarks records Llama 3.1 70B Q4_K_M at 42.5 GB on the A6000; DatabaseMart measures Llama 3.3 70B Q4_K_M at 43.7 GB and DeepSeek-R1 70B at 43.2 GB on the same card. On the Radeon, llm-tracker records Llama 3.1 70B Q4_K_M at 46.0 GB and Llama 3 70B Q4_K_M at 45.0 GB under llama.cpp.
That spread — 42.5 GB to 46.0 GB for the same class of model — is runtime allocation behaviour, and it is the difference between 5.5 GB of context headroom and 2 GB. It is also why Q5_K_M is not a realistic step up on 48 GB: the derived ~50 GB is over the ceiling before a single token of cache is allocated.
No published run puts both cards through identical hardware and software. What exists is a set of independent measurements at three model scales, plus one genuinely matched pair: llama.cpp's cross-vendor benchmark threads, which run the same harness and the same Llama 2 7B Q4_0 quant on each card.
| Model / quant | Runtime | Radeon Pro W7900 | RTX A6000 | Source |
|---|---|---|---|---|
| Llama 2 7B Q4_0 — generation | llama.cpp | 127.43 tok/s | 144.87 tok/s | llama.cpp #15021 / #15013 |
| Llama 2 7B Q4_0 — prefill | llama.cpp | 3,472.86 tok/s | 5,662.39 tok/s | llama.cpp #15021 / #15013 |
| Llama 3.x 8B Q4/Q5 — generation | llama.cpp | 96.00 tok/s | 102.22 tok/s | llm-tracker / OpenLLM Benchmarks |
| Llama 3.x 8B — prefill | llama.cpp | 2,689.00 tok/s | 3,621.81 tok/s | llm-tracker / OpenLLM Benchmarks |
| 32B-class Q4_K_M — generation | Ollama / llama.cpp | 19.80 tok/s (Q8_0) | 26.08 tok/s | Tom's Hardware / DatabaseMart |
| Llama 3.1 70B Q4_K_M — generation | llama.cpp | 11.35 tok/s | 14.60 tok/s | llm-tracker / OpenLLM Benchmarks |
| Llama 3.1 70B Q4_K_M — prefill | llama.cpp | 297.00 tok/s | 467.00 tok/s | llm-tracker / OpenLLM Benchmarks |
| Llama 3.3 70B Q4_K_M — generation | Ollama | — | 13.56 tok/s | DatabaseMart |
| DeepSeek-R1 70B Q4_K_M — generation | llama.cpp / Ollama | 12.70 tok/s | 13.65-14.10 tok/s | Tom's Hardware / DatabaseMart |
| Llama 3 70B Q4_K_M @ 4K ctx — generation | llama.cpp | 11.34 tok/s | 14.58 tok/s | llm-tracker / XiongjieDai |
The pattern is uniform across every row and every source: the A6000 leads generation by roughly 12-29% and prefill by roughly 1.5-1.6×. It leads on the matched llama.cpp 7B test, where the code path is as close to identical as this comparison gets, and it leads on the 70B runs that actually motivate a 48 GB purchase.
Two figures are worth reading carefully rather than at face value. DatabaseMart's vLLM A6000 results show Llama 3.1 8B at FP16 hitting 1,218.63 tok/s — that is aggregate batched throughput across concurrent requests, not single-stream speed, and it is not comparable to the llama.cpp single-stream numbers above. Similarly, Tom's Hardware's W7900 coverage frames the card as beating NVIDIA's 24 GB parts, which is a different and entirely fair claim — a 24 GB card cannot run 70B at Q4 at all.
The W7900 is a gfx1100 part and it is officially supported by ROCm, which puts it in a materially better position than consumer RDNA cards. AMD's ROCm system requirements lists the supported hardware, and the W7900 belongs there. llama.cpp builds against HIP, vLLM has a documented ROCm path in its GPU installation guide, and both run.
The cost is not "does it work." It is version coupling. Container base images across the local-inference ecosystem are built CUDA-first; Python wheels ship CUDA builds by default; kernel-module, ROCm-runtime and userspace versions have to move together. In practice that means pinning versions, reading release notes before upgrades, and occasionally building from source — engineering time the CUDA path mostly does not ask for.
The measured consequence is visible in the runtime spread. On the same W7900 running the same Llama 3 70B model, llm-tracker's project log records:
| Runtime | Quant | Prefill | Generation | VRAM |
|---|---|---|---|---|
| llama.cpp | Q4_K_M | 255.59 tok/s | 11.34 tok/s | 45.0 GB |
| MLC-LLM | q4f16_1 | 95.50 tok/s | 10.70 tok/s | 42.8 GB |
| MLC-LLM | Q4_0 | 36.91 tok/s | 12.04 tok/s | 42.8 GB |
| ExLlamaV2 | Q4_K_M | 430.62 tok/s | 7.03 tok/s | 39.0 GB |
Generation varies from 7.03 to 12.04 tok/s — a 71% spread — purely on runtime choice, with prefill swinging by more than 11×. Picking the wrong runtime on the Radeon costs more performance than the entire hardware gap to the A6000. On the NVIDIA side that variance exists too, but the default choice is usually close to the best one.
That is the honest version of the ROCm question in 2026: not immature, but still the path where your configuration decisions matter more than your hardware.
Every request splits into a compute-bound phase and a memory-bound one, and these two cards sit on opposite sides of that split on paper.
Prefill processes your prompt in parallel and scales with compute. The A6000's 10,752 CUDA cores against the W7900's 6,144 shading units predicts an NVIDIA win, and the measurements agree emphatically: 5,662.39 against 3,472.86 tok/s on the matched 7B test, 467 against 297 tok/s on 70B.
Generation emits one token at a time, reading the full weight set per token, and scales with bandwidth. The W7900's 864.0 GB/s against the A6000's 768.0 GB/s predicts an AMD win of about 12.5%. The measurements show the opposite — a 12-29% NVIDIA lead — which means the Radeon is converting substantially less of its bandwidth into tokens.
The practical read: if your workload is long prompts with short answers — document analysis, retrieval-augmented generation, code review over a large file — prefill dominates and the A6000's 1.6× advantage is the whole experience. If it is long conversational generation from short prompts, generation dominates and the gap narrows to roughly a quarter.
Neither pattern favours the Radeon.
With a 70B model at Q4_K_M consuming 42.5-46.0 GB, the remaining 2-5.5 GB is the entire context budget. That is a meaningfully tighter constraint than the frame-buffer number suggests, and it is why 70B on one card is a "medium context" configuration rather than a long-context one.
Context falloff on a 48 GB card is measurable even when nothing spills. hardware-corner.net records Qwen3 32B Q4_K_M on the A6000 at 704.82 tok/s prefill and 18.32 tok/s generation at 16,384 context, while DatabaseMart's shorter-context Ollama runs put the same 32B class at 26.08 tok/s. A 30% generation loss between short and 16K context, on a model with 15 GB of frame buffer to spare, is the cache doing its work.
Two mitigations are worth configuring before you buy more hardware. Eight-bit KV-cache quantization roughly halves cache footprint at negligible quality cost, and on a card with 3 GB of headroom that is the difference between 4K and 8K working context. Dropping to Q3_K_M frees roughly 8 GB of weights, which buys a genuinely long context at a quality cost most people notice on reasoning tasks and not on summarisation.
What does not work is running Q5_K_M and hoping. The derived ~50 GB exceeds the frame buffer before any cache is allocated.
A 48 GB card is roughly ten times the price of a 12 GB one, and the honest comparison is worth making before the money moves.
A ZOTAC Gaming GeForce RTX 3060 Twin Edge 12GB — or its MSI GeForce RTX 3060 Ventus 2X 12G sibling — holds a 14B model at Q4_K_M and runs it usefully. llmrun.dev measures Phi-4 14B Q4_K_M at 24.6 tok/s in 9.5 GB on that card. Twenty-five tokens per second is faster than most people read.
What the 12 GB tier cannot do is the thing this article is about. A 70B model at Q4 needs roughly 42 GB of weights; there is no quantization that fits it into 12 GB without offloading most of the model to system RAM, at which point generation drops into low single digits. The step from 14B to 70B is a step from "fits on a $300 card" to "needs a four-figure one," with nothing useful in between on a single board.
So the question to answer before spending is whether a 70B model actually does something a 14B or 32B model does not, for your workload. For long-form reasoning and multi-step agent chains, often yes. For summarisation, classification, code completion and chat, frequently no. The SpecPicks RTX 3060 benchmark page covers what the entry tier delivers, and the SpecPicks 70B GPU guide covers the alternatives at this end.
CPU. Prompt tokenisation, sampling and any offloaded layer run on the host. The AMD Ryzen 7 5800X is a sensible AM4 floor at eight cores and sixteen threads — PassMark rates it at 27,679 CPU Mark with a 3,448 single-thread score, and full specifications are on AMD's product page. Single-thread performance matters more than core count here, because the host-side work is largely serial.
Storage. A 70B model at Q4_K_M is a ~42 GB file, and a working library of two or three of them plus their smaller siblings fills a drive quickly. The Kingston A400 960GB SATA SSD is the cheap way to hold that library. Drive speed affects load time only — once weights are in VRAM, storage is out of the loop entirely — so this is the component to buy on price per gigabyte.
Cooling. Inference holds a card near its ceiling for as long as the queue lasts, which is a different thermal profile from a two-hour gaming session. The Noctua NH-U12S is the quiet-at-sustained-load answer on the CPU side; a large tower running slowly beats a small cooler running fast when the load never ends. The GPU side is harder, because both of these cards use blowers designed for a workstation chassis with front-to-back airflow.
Power. Both cards are roughly 300 W parts. With a real CPU behind one, an 850 W unit is the sensible floor, and quality matters more than headroom on a machine that runs continuously.
The A6000's price advantage in 2026 comes entirely from the second-hand market, and second-hand workstation GPUs carry a specific risk profile.
Blower health. These cards use single-blower coolers that spin continuously under load. A card that spent three years in a rack has a blower with three years of bearing wear. Ask for a recording of the fan at load, and price the card as though you may need to recondition or replace the cooler.
Power-on hours and provenance. Sellers who cannot say where a card came from are usually clearing datacenter decommissions. That is not disqualifying — it is often the cheapest supply — but it should be reflected in the price.
ECC state. The A6000 carries ECC memory. Confirm it is enabled and reporting clean; a card with a history of corrected errors is telling you something about its memory.
Warranty transfer. NVIDIA's professional warranties generally do not transfer to a second owner. Assume you have none.
Seller channel. Buy only from sellers who accept returns, and test the card under sustained inference load — not a five-minute benchmark — inside the return window. A 70B model on a loop for two hours is the correct acceptance test.
The PNY RTX A6000 48GB is the reference SKU to price against; because these trade primarily second-hand, the eBay listing is usually the live market rather than the retail one.
Get the RTX A6000 if… the machine's job is 70B inference and you want the shortest path from unboxing to working. It leads generation by 12-29% and prefill by roughly 1.6× across every source above, the CUDA path is the default target for llama.cpp, vLLM and TensorRT-LLM alike, and the used market makes it the cheaper entry to 48 GB. Accept the used-hardware diligence in exchange.
Get the Radeon Pro W7900 if… you want a new card with a warranty, your workload includes rasterisation or professional visualisation where its 27,729 PassMark G3D rating genuinely leads, or you have institutional reasons to stay on an open software stack. Eleven to twelve tokens per second on 70B is usable. Budget the setup time honestly.
Get two 24 GB cards instead if… throughput per dollar is the metric and you are comfortable maintaining a sharded configuration. You will get more tokens per second for the same money at the cost of double the power, two slots, and a configuration that breaks on runtime upgrades. The SpecPicks 24 GB guide and dual-GPU comparison cover that path.
The RTX A6000 is the pick for local 70B inference, on the strength of measurements rather than specifications. Fourteen-point-six tokens per second against 11.35, 467 tok/s prefill against 297, and a software stack that most local-inference projects target first — that is a consistent lead across five independent sources, and it holds at every model size tested.
The counter-case is straightforward and honest. The W7900 is newer silicon, sold new with a warranty, with more memory bandwidth and a clear lead in rasterisation synthetics. It is officially ROCm-supported, which distinguishes it sharply from consumer Radeon cards. If your purchasing rules exclude used hardware, or your workload spans professional graphics as well as inference, it is a defensible buy at a real but bounded performance cost. Just do not expect the bandwidth advantage to appear in your tokens per second, because across every source cited here it does not.
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.
The head-to-head spec and price view is at Radeon Pro W7900 vs RTX A6000.
Because the A6000 trades primarily second-hand, the PNY RTX A6000 48GB listing surfaces eBay as the live market with an Amazon fallback. The consumer alternatives referenced above — the ZOTAC Gaming GeForce RTX 3060 Twin Edge 12GB and the MSI GeForce RTX 3060 Ventus 2X 12G — carry standard Amazon CTAs, as do the host parts: the AMD Ryzen 7 5800X, the Kingston A400 960GB SATA SSD, and the Noctua NH-U12S.
Prices shown were last tracked at crawl time and may vary — check the listing for the current price. As an Amazon Associate, SpecPicks earns from qualifying purchases; we also earn on qualifying eBay purchases via the eBay Partner Network.
These figures are for 7–9B models, not the 70B models this article covers. The table holds model size fixed so the cards can be compared with each other; for the size in the title, use the article's own figures.
Median generation throughput at 7–9B models (Llama 3.1 8B, Qwen 3 8B), Q4 quantization, from community-reported runs SpecPicks tracks. Each row pools runs from different sources, runtimes and models in that class, so the rows are not a matched head-to-head; where the article compares cards on the same rig, its own figures are the like-for-like result. Street price is the second-lowest listing priced within the last 24 hours at 0.5–2× launch MSRP, so no single listing sets it, and a street figure well above MSRP says so in its cell; no card has enough listings passing that check, so the column shows launch MSRP. Rows marked for comparison are not the cards this article is about — they are included so the throughput column has something to be read against.
| GPU | VRAM | Llama-3-8B class, Q4 | Launch MSRP | Sources |
|---|---|---|---|---|
| NVIDIA RTX A6000 48GB | 48 GB | 102.2 tok/s3 runs · 3 sources | $4,650MSRP | SpecPicks median of 3 runs; sources: llama.cpp GitHub Discussion #15013, OpenLLMBenchmarks, XiongjieDai GPU-Benchmarks-on-LLM… |
| AMD Radeon Pro W7900 48GB | 48 GB | 93.8 tok/s6 runs · 2 sources | $3,999MSRP | SpecPicks median of 6 runs; sources: llm-tracker.info (lhl) — vLLM…, llama.cpp GitHub Discussions |
| RTX 6000 Ada Generationfor comparison | 48 GB | 132.4 tok/s4 runs · 4 sources | $6,800MSRP | SpecPicks median of 4 runs; sources: GPU-Benchmarks-on-LLM-Inference…, Hardware Corner, llama.cpp GitHub (CUDA performanc…, MyAIHardware |
Amazon & eBay listings, plus full specs and alternatives on each product page.
As an Amazon Associate, SpecPicks earns from qualifying purchases; we also earn on qualifying eBay purchases via the eBay Partner Network. Prices shown were last tracked at crawl time and may vary — check the listing for the current price.
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