RX 7900 XTX 24GB vs RTX 4080 16GB for Local LLM Inference (2026)

Key Takeaways

• 24 GB versus 16 GB is the whole argument. Per TechPowerUp, the RX 7900 XTX ships 24 GB of GDDR6; the RTX 4080 ships 16 GB of GDDR6X per TechPowerUp. That difference decides which model classes stay resident.
• The 7900 XTX runs a 32B model that the 4080 cannot hold. Local AI Master reports 26 tok/s for DeepSeek-R1 32B at q4_K_M on the Radeon. On the 4080, Rost Glukhov's 16 GB VRAM series shows a 24B model already needing about 19 GB and collapsing to 18.51 tok/s.
• Inside 16 GB, the 4080 is the faster card. Hardware Corner logs 102.70 tok/s on Qwen3 8B q4_K_M at 4K context on the RTX 4080, against 96 tok/s for Llama 3.1 8B at Q5_K_M on the Radeon per Local AI Master.
• Prompt processing is where CUDA's lead is widest. The llama.cpp community scoreboard puts the 4080 at 8,031 tok/s prefill on Llama-2 7B q4_0 (discussion #15013) versus 3,874 tok/s for the 7900 XTX on the same test (discussion #15021).
• Two 12 GB cards are a real third option. A pair of RTX 3060 12 GB boards reaches the same 24 GB aggregate; TechPowerUp lists the 3060 at a $329 launch price, and Hardware Corner measures 55.20 tok/s on Qwen3 8B q4_K_XL per card at 4K context on the RTX 3060 12 GB.
• The board-power gap is small but permanent. TechPowerUp lists 355 W for the Radeon against 320 W for the GeForce — a 35 W delta that compounds on a machine that never sleeps.

Spec delta: what actually differs between these two cards

Every figure in that table is drawn from the TechPowerUp database entries for the Radeon RX 7900 XTX and the GeForce RTX 4080, cross-checked against AMD's RX 7900 XTX product page and NVIDIA's RTX 40-series page. The shape of the delta is unusual: the Radeon wins memory on both axes, the GeForce wins compute, and they cost about the same. That is precisely why the answer depends on which half of an inference request dominates your workload.

The buy links: Sapphire Pulse RX 7900 XTX 24 GB and MSI RTX 4080 16 GB Ventus 3X OC.

What model sizes fit in 24GB vs 16GB?

Weights are the floor, not the total. A GGUF at a given quantization rung occupies roughly params × bits-per-weight ÷ 8 gigabytes, and then KV cache, the compute buffer, and the desktop's own framebuffer sit on top. Budget 1.5–2 GB of overhead on a headless box and more if the same card is drawing your monitor.

Read the two right-hand columns as the actual product difference. At Q4_K_M — the rung most people settle on, because it is where quality loss stops being obvious in ordinary use — the 24 GB card holds a 32B model with roughly 5 GB left for context, and the 16 GB card tops out at 14B. Glukhov's 16 GB measurements confirm the boundary empirically: Qwen3 14B q4_K_M sits at about 12 GB of VRAM on the 4080, while a 24B model reports about 19 GB and a 30B coder model about 20 GB, both of which exceed the card (source).

The quality cost of dropping a rung is not linear. Going from Q8_0 to Q4_K_M is broadly accepted as a small perplexity penalty for a large memory saving; going below Q3 degrades instruction-following and long-form coherence noticeably. So "just quantize harder" is a real option on the 4080 for a 32B model, but you are trading measurable model quality to work around a hardware limit that the other card does not have.

How much faster is one card than the other in tokens per second?

Public figures come from different rigs, backends and prompt lengths, so treat the column as a range rather than a head-to-head lab result. Where sources disagree, both are listed.

Two patterns fall out. First, in the 7B–14B band the cards are close enough that the difference is a preference, not a decision — the Radeon's bandwidth advantage and the GeForce's compute advantage roughly cancel, and backend maturity swings individual results either way. LocalScore's accelerator page for the 7900 XTX reports 51.30 tok/s on Llama 3.1 8B Q4_K_M under its own harness, well below the Ollama figures above, which is a useful reminder that harness and prompt length matter as much as silicon.

Second, the moment a model crosses 16 GB the comparison stops being a comparison. The Radeon's 26 tok/s on a 32B model and the GeForce's 18.51 tok/s on a smaller 24B model are not two points on one curve; the Radeon is running from VRAM and the GeForce is not.

Does ROCm still cost you throughput versus CUDA in 2026?

Less than its reputation suggests for mainstream inference, and more than zero at the edges.

On the core path, the community numbers do not show a systematic ROCm penalty in token generation. The llama.cpp ROCm/HIP performance discussion records 170.12 tok/s for the 7900 XTX on Llama-2 7B q4_0, above the 142.49 tok/s the CUDA scoreboard thread records for the 4080 on the same test. The 1337hero ROCm benchmark repository similarly logs 118.82 tok/s on Llama 3 8B q4_0. Generation is memory-bound, the Radeon has more bandwidth, and the ROCm backend is mature enough to convert that into tokens.

Prefill is where the picture changes. The same two llama.cpp threads put prompt processing at 8,031.64 tok/s for the 4080 against 3,874.25 tok/s for the 7900 XTX — roughly a 2× CUDA advantage on the compute-bound half of the request. That gap tracks the shader-count difference and the maturity of NVIDIA's kernel path.

The remaining friction is ecosystem rather than throughput. Ollama, LM Studio and llama.cpp all ship working ROCm or Vulkan paths for RDNA 3, so ordinary chat and RAG work runs without patching. Newer research tooling, some vision stacks, and certain quantization formats still land on CUDA first. Per the sources above, no claim here should be read as ROCm being broadly slower — it is narrower in coverage, which is a different problem with a different fix: check your specific runtime's support matrix before you buy.

Prefill versus generation: which card wins which half of the request?

Every request has two phases with opposite bottlenecks.

Prefill (prompt processing) reads your entire prompt and builds the KV cache. It is compute-bound and embarrassingly parallel, so it scales with shader throughput. The 4080's 9,728 CUDA cores against the 7900 XTX's 6,144 stream processors is exactly the gap the prefill numbers reflect.

Generation (decode) emits one token at a time, and each token requires streaming the model's weights through the memory subsystem. It is bandwidth-bound. The Radeon's 960.0 GB/s against 716.8 GB/s is exactly the gap the generation numbers reflect.

Which one you feel depends on your prompt-to-output ratio:

• Chat and coding assistance — short prompts, long answers. Generation dominates; the Radeon's bandwidth is worth more.
• RAG over retrieved documents — 4K–16K tokens of context per request, a few hundred tokens of output. Prefill dominates; the 4080's compute advantage shows up as visibly lower time-to-first-token.
• Batch summarization or classification — long inputs, short outputs, run repeatedly. This is the workload where the 4080's roughly 2× prefill lead compounds hardest.

If your daily driver is a RAG pipeline stuffing 8K of retrieved context into every call, the CUDA card genuinely feels snappier despite losing the throughput table — right up until your chosen model outgrows 16 GB.

What happens to throughput at 8K, 32K and 128K context?

KV cache scales linearly with context length and is the second claim on VRAM after weights. A rough working figure for a modern 7–8B model at FP16 cache is about 0.5 GB per 4K of context; a 14B model runs roughly double that, and a 32B model roughly triple.

The practical consequence: on a 16 GB card, context is what breaks first, not model size. A 14B model that loads with 3.5 GB spare at 4K has consumed that headroom by 16K. Hardware Corner's own 4080 figures show the shape of the cost even when everything still fits — Qwen3 8B q4_K_M runs 102.70 tok/s at 4K context but 77.90 tok/s at 16K, a 24% drop purely from cache pressure and longer attention (source).

Quantizing the KV cache to 8-bit reclaims roughly half of the cache footprint at modest quality cost and is the first lever to pull on either card. On the 24 GB card it buys you a long-context 32B setup; on the 16 GB card it buys you a workable 32K window on a 14B model.

Can you run two of these instead of one bigger card?

For pure inference, aggregate VRAM is what counts, and llama.cpp splits layers across GPUs without drama. That makes a pair of cheaper 12 GB boards a legitimate competitor to either flagship.

Two MSI RTX 3060 Ventus 2X 12G cards give you the same 24 GB the Radeon has, at a launch MSRP of $329 each per TechPowerUp. Per-card throughput is lower — Hardware Corner measures 55.20 tok/s on Qwen3 8B q4_K_XL at 4K context on the RTX 3060 12 GB — and layer-split inference runs at roughly the speed of the slower card rather than summing throughput. What you buy is capacity, not speed.

The costs are concrete: you need a board with two usable x8 slots, case airflow for two cards, and you carry two cards' idle draw permanently. Single-GPU workloads — gaming, most image generation, anything that will not shard — see none of the second card's memory. If you want one machine that games at 4K and hosts a 32B model, the single 24 GB card is the cleaner answer. If the box only ever serves models, the pair is better value per gigabyte. That build is worked out in detail in Best Parts for a Dual RTX 3060 24GB Local-LLM Build in 2026.

What does the rest of the rig need to keep up?

Host CPU. For fully-resident inference the CPU barely matters — it feeds the GPU and gets out of the way. It starts mattering the moment you offload, because CPU-resident layers then set the pace for every token. An AMD Ryzen 7 5800X — 8 cores, 16 threads, 105 W TDP, 4.7 GHz boost per AMD's product page — is a sensible floor for a machine that will sometimes spill a model into system RAM, and Phoronix's AI testing of that part records 18.50 tok/s on a Llama 3.1 70B q4_K_M configuration using 42.1 GB of memory. It also gives you enough single-thread headroom that tokenization on a long prompt is never the bottleneck. Pair it with at least 32 GB of dual-channel DDR4; 64 GB if you intend to offload 70B-class models at all.

Model-library storage. This is the line item people underestimate. A 32B model at Q4_K_M is roughly 19 GB; a 70B at the same rung is around 42 GB. Anyone experimenting seriously ends up holding several base models, a couple of quantization rungs of their favourite, an embedding model and maybe a vision model. Thirty models is a realistic collection and lands well past 500 GB.

Capacity beats peak sequential throughput here, because models load once per session and then live in VRAM. A Kingston A400 960 GB SATA SSD — listed at $129.99 in the SpecPicks catalog as of 2026-09-09, though prices move — is enough to hold a serious working set at low cost, with cold-load time dominated by file size rather than interface generation. The interfaces are compared directly in NVMe vs SATA SSD for Local LLM Model Libraries in 2026.

Performance per dollar and per watt

Using launch MSRP for a stable denominator and the Qwen 14B q4_K_M figures — the largest model both cards run resident — both land at 62 tok/s per the sources above.

The Radeon wins dollars-per-token and dollars-per-gigabyte; the GeForce wins tokens-per-watt. On the energy question, the 35 W board-power delta at 10 hours a day and $0.16/kWh works out to roughly $20 a year — real but not a tiebreaker. Idle draw matters more on an always-on box than peak board power does, which is the argument made in Cooling a 24/7 Local LLM Rig: Air vs 120mm AIO vs 240mm AIO.

The cost-per-GB row is the one to sit with. Paying 80% more per gigabyte of VRAM is a bad trade in a workload where VRAM is the binding constraint.

Common pitfalls

• Sizing the card to the weights and forgetting the cache. A 14B model at Q4_K_M "fits" 16 GB at 4K context and does not at 32K. Budget context alongside weights.
• Assuming ROCm means slow. The generation numbers above do not support that. What ROCm means is narrower library coverage — verify your specific runtime, not the stack's reputation.
• Running the model on the card driving your monitor. A desktop compositor and a browser can hold 1–2 GB of VRAM. On a 16 GB card that is the margin between resident and offloaded.
• Benchmarking with a 512-token prompt and planning for 8K. Short-prompt figures flatter every card. The llama.cpp scoreboards are explicit about context length for exactly this reason.
• Buying a flagship for an 8B workload. If your daily driver fits in 12 GB, the throughput gain from a $1,000 card over a $329 one is far smaller than the price gap suggests.

When NOT to buy either card

Skip both if your models all fit comfortably in 12 GB today. Hardware Corner's 3060 12 GB figures — 55.20 tok/s on Qwen3 8B q4_K_XL at 4K (source) — are perfectly usable for chat and autocomplete, at a third of the price. Skip both, too, if what you actually want is a 70B model at full speed: neither card holds one, and the Radeon's 13.37 tok/s offloaded result (1337hero) is the shape of that compromise. And skip both if your workflow depends on a specific fine-tuning or vision library — check that library's support matrix first, because on the Radeon the answer is sometimes no, and on a 16 GB card the answer is sometimes "not at this model size."

Verdict matrix

Get the RX 7900 XTX if…

• Your target is a 27B–32B model held resident at Q4_K_M — this is the only one of the two that does it, at 26–27 tok/s per Local AI Master.
• Your workload is chat- or coding-shaped: short prompts, long generations, bandwidth-bound.
• You want the lower cost per gigabyte of VRAM and are comfortable checking library support before installing something new.
• You also game at 4K and want one card that does not compromise on either job.

Get the RTX 4080 if…

• Your pipeline is prefill-heavy — long retrieved contexts, short answers — and the roughly 2× prompt-processing lead is what you feel every day.
• You depend on CUDA-only tooling: custom kernels, a specific fine-tuning stack, or a vision library that ships NVIDIA-first.
• Your models genuinely fit in 16 GB and you would rather have the faster path on 8B–14B than headroom you will not use.
• Tokens per watt matters more to you than tokens per dollar.

Get neither, get two 12 GB cards if…

• The box is a dedicated inference server and will never need one card's worth of VRAM for a single non-shardable job.
• You want 24 GB aggregate for well under either flagship's price — $329 launch MSRP each per TechPowerUp.
• Your motherboard has two usable x8 slots and your case has the airflow.

The recommended pick

For most people buying one card for local LLM work in 2026, the RX 7900 XTX is the right answer, and the reason is narrow and specific: it is the cheaper of the two per gigabyte and it is the only one that holds a 32B-class model at Q4_K_M without offload. Every throughput comparison in the 7B–14B band is close enough to be a wash; the capacity comparison is not close at all.

The condition that flips it is tooling. If any part of your workflow requires CUDA — a research repo with hand-written kernels, a fine-tuning stack that has never shipped a ROCm path, a vision model whose only working implementation targets NVIDIA — buy the RTX 4080 and accept the 16 GB ceiling. That is not a performance argument; it is a compatibility one, and compatibility problems do not resolve themselves after purchase.

Bottom line

These two cards cost roughly the same and answer different questions. The 4080 asks "how fast can I process a long prompt," and answers it well: 8,031 tok/s prefill on a 7B model, roughly double the Radeon's, plus the widest software compatibility in the category. The 7900 XTX asks "what is the largest model I can keep in memory," and answers with a 32B model at 26 tok/s that the GeForce simply cannot hold. For local inference the second question is the one that determines what your machine can do, and the first only determines how quickly it does it. Decide your model class, then buy the card that holds it.

Related guides

• RTX 3090 vs RTX 4090 for LLM Inference: Same 24GB (2026)
• Best Parts for a Dual RTX 3060 24GB Local-LLM Build in 2026
• Local LLM Setup in 2026: AMD GPU Guide by Model Size
• Ollama vs vLLM vs llama.cpp on a 12GB GPU: Which Wins for One User?
• Local LLM Autocomplete + Agentic Coding on 16GB VRAM

Live price comparison

Both cards, side by side with live pricing, current Product schema and Amazon CTAs for each: RX 7900 XTX vs RTX 4080 head-to-head. Prices on that page refresh from the catalog and may differ from the figures quoted above.

Also referenced in this piece: the ZOTAC RTX 3060 Twin Edge OC 12 GB as an alternate dual-card board.

Citations and sources

• TechPowerUp — Radeon RX 7900 XTX specifications (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 4080 specifications (accessed 2026-09-09)
• TechPowerUp — GeForce RTX 3060 12 GB specifications (accessed 2026-09-09)
• AMD — Radeon RX 7900 XTX product page (accessed 2026-09-09)
• AMD — Ryzen 7 5800X product page (accessed 2026-09-09)
• NVIDIA — GeForce RTX 40 Series (accessed 2026-09-09)
• llama.cpp GitHub — CUDA performance discussion #15013 (accessed 2026-09-09)
• llama.cpp GitHub — ROCm/HIP performance discussion #15021 (accessed 2026-09-09)
• 1337hero — rx7900xtx-llama-bench-rocm (accessed 2026-09-09)
• XiongjieDai — GPU-Benchmarks-on-LLM-Inference (accessed 2026-09-09)
• Hardware Corner — RTX 4080 LLM benchmarks (accessed 2026-09-09)
• Hardware Corner — RTX 3060 12 GB LLM benchmarks (accessed 2026-09-09)
• Rost Glukhov — Choosing the best LLM for Ollama on a 16 GB VRAM GPU (accessed 2026-09-09)
• Local AI Master — Radeon RX 7900 XTX for local AI (accessed 2026-09-09)
• Markaicode — Benchmarking local LLM token speed on NVIDIA RTX (accessed 2026-09-09)
• Kunal Ganglani — LLM benchmarks (accessed 2026-09-09)
• LocalScore — Radeon RX 7900 XTX accelerator page (accessed 2026-09-09)
• Phoronix — AMD Ryzen 7 5800X AI testing (accessed 2026-09-09)

This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.