Step 0: do you need 48 GB on one card, or two 24 GB cards?
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.
Key takeaways
- On 70B Q4_K_M, the A6000 lands at 13.56-14.60 tok/s across three independent sources — DatabaseMart (13.56 tok/s, 43.7 GB), OpenLLM Benchmarks (14.60 tok/s, 42.5 GB), and XiongjieDai's llama.cpp results (14.58 tok/s).
- The W7900 lands at 11.34-12.70 tok/s on the same class of workload, per llm-tracker.info (11.35, 46.0 GB) and Tom's Hardware (12.70 on DeepSeek-R1 70B).
- The W7900 carries more memory bandwidth (864.0 GB/s vs 768.0 GB/s, per TechPowerUp) and beats the A6000 in PassMark's G3D rating — 27,729 to 22,767 points. Neither advantage shows up in inference.
- Prefill is where the gap widens. On matched llama.cpp 7B Q4_0 runs, the A6000 processes 5,662.39 tok/s (discussion #15013) against the W7900's 3,472.86 tok/s (discussion #15021) — 1.63×.
- Runtime choice moves the W7900 more than the hardware does: the same 70B model runs 11.34 tok/s under llama.cpp and 7.03 tok/s under ExLlamaV2 on the same card, per llm-tracker's W7900 project log.
- Both are roughly 300 W blower cards designed for workstation chassis, not glass-panel gaming cases.
Spec delta: Radeon Pro W7900 vs RTX A6000
| 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.
What fits in 48 GB?
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.
How fast is each card in practice?
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.
CUDA versus ROCm: what the software stack costs you
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.
Prefill vs generation: two bottlenecks in one request
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.
Context length and KV cache at 48 GB
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.
Do you actually need this tier? The 12 GB reality check
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.
Building the host around a 48 GB card
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.
Buying used: what to check before you wire money for an A6000
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.
Verdict matrix
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.
Bottom line
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.
Related guides
- NVIDIA RTX A6000 48GB local LLM review — the single-card deep dive
- RTX PRO 6000 Blackwell vs A6000 — what the generational step buys
- Best GPU for Llama 70B locally — the wider field at this workload
- Best 24 GB GPU for local LLMs — the tier below
- Dual RTX 3060 vs a single GPU for Llama 70B — the multi-card alternative
- W7900 benchmark data and A6000 benchmark data
Citations and sources
- TechPowerUp — Radeon Pro W7900 specifications (accessed 2026-09-10)
- TechPowerUp — RTX A6000 specifications (accessed 2026-09-10)
- AMD — Radeon Pro W7900 product page (accessed 2026-09-10)
- NVIDIA — RTX A6000 product page (accessed 2026-09-10)
- llm-tracker.info — AMD GPUs local inference guide and benchmarks (accessed 2026-09-10)
- llm-tracker.info — W7900 Pervasive Computing Project runtime comparison (accessed 2026-09-10)
- OpenLLM Benchmarks — RTX A6000 48GB LLM inference performance and value (accessed 2026-09-10)
- DatabaseMart — Ollama GPU benchmark on the A6000 (accessed 2026-09-10)
- DatabaseMart — vLLM GPU benchmark on the A6000 (accessed 2026-09-10)
- Tom's Hardware — AMD RDNA 3 professional GPUs with 48GB in AI workloads (accessed 2026-09-10)
- llama.cpp GitHub Discussion #15013 — NVIDIA benchmark thread (accessed 2026-09-10)
- llama.cpp GitHub Discussion #15021 — AMD benchmark thread (accessed 2026-09-10)
- GPU-Benchmarks-on-LLM-Inference (XiongjieDai) — 70B llama.cpp results (accessed 2026-09-10)
- hardware-corner.net — GPU ranking for local LLMs at 16K context (accessed 2026-09-10)
- PassMark — Radeon PRO W7900 G3D Mark (accessed 2026-09-10)
- PassMark — RTX A6000 G3D Mark (accessed 2026-09-10)
- TopCPU — 3DMark Time Spy GPU rankings (accessed 2026-09-10)
- AMD — ROCm system requirements and supported GPUs (accessed 2026-09-10)
- vLLM — GPU installation guide (accessed 2026-09-10)
- llmrun.dev — RTX 3060 12GB model benchmarks (accessed 2026-09-10)
- PassMark — AMD Ryzen 7 5800X CPU Mark (accessed 2026-09-10)
- AMD — Ryzen 7 5800X product page (accessed 2026-09-10)
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.
Live price comparison
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.
