RTX 4080 Super vs RX 7900 XTX for local AI in 2026
16 GB Ada; the awkward middle child of the Ada lineup.
- VRAM
- 16 GB
- Bandwidth
- 736 GB/s
- TDP
- 320 W
- Price
- $900-1,100 (2026; new + lightly used)
24 GB AMD flagship; ROCm + Vulkan path.
- VRAM
- 24 GB
- Bandwidth
- 960 GB/s
- TDP
- 355 W
- Price
- $700-900 (2026 retail)
RTX 4080 Super
16 GB Ada; the awkward middle child of the Ada lineup.
RX 7900 XTX
24 GB AMD flagship; ROCm + Vulkan path.
The $700-1,100 buyer decision in 2026: NVIDIA RTX 4080 Super at 16 GB or AMD RX 7900 XTX at 24 GB. The 7900 XTX has more VRAM, lower price, and is the dollar-efficient pick on paper. The 4080 Super has CUDA, broader runtime support, and better tooling for production.
VRAM tells the loadout story. 24 GB on the 7900 XTX fits 70B Q4 with tight context; 16 GB on the 4080 Super does not. For 7B-32B daily use either card works, but the 7900 XTX's headroom matters when models grow or context expands.
Software is the friction tax. ROCm in 2026 is meaningfully better than 2023 — vLLM works, llama.cpp ROCm and Vulkan work, Ollama works. But SGLang, TensorRT-LLM, and bleeding-edge HF wheels still default CUDA-first. AMD's day-zero gap on new releases is real, often days to weeks.
If you'd happily run llama.cpp + Ollama on Linux and save $300-400, the 7900 XTX is correct. If your workflow touches vLLM / SGLang / TensorRT-LLM in production, the NVIDIA tax pays for itself.
Who wins each workload
Each row is a workload local-AI operators actually run. Verdicts derived from VRAM math + bandwidth — no editorial hand-wave.
What each card actually runs
VRAM math against a canonical set of popular models. The largest context window that fits with headroom appears in each cell.
| Model | RTX 4080 Super | RX 7900 XTX |
|---|---|---|
Qwen 3 14B Q4_K_M 14B params · Q4_K_M | 16K ctx, tight | 32K ctx |
Qwen 3 32B Q4_K_M 32B params · Q4_K_M | OOM | 4K ctx, tight |
Llama 3.3 70B Q4_K_M 70B params · Q4_K_M | OOM | OOM |
DeepSeek R1 distill 32B 32B params · Q4_K_M | OOM | 2K only |
Mixtral 8x22B Q4 141B params · Q4_K_M | OOM | OOM |
FLUX.1 image gen 12B params · FP16 | OOM | OOM |
Llama 3.3 70B Q4_K_M
Computed from each option's sustained TDP × predicted tok/s at $0.16/kWh. Cloud baseline: Claude Sonnet 4.6 (input + output).
Electricity-only cost — excludes the upfront hardware purchase, cooling, and amortized component depreciation. Hardware ROI math lives at /cost-vs-cloud; this line is for "is the marginal token cheaper than Claude?" not "should I buy this rig instead of paying Anthropic." MODELED ESTIMATE.
Quick decision rules
Operational matrix
| Dimension | RTX 4080 Super 16 GB Ada; the awkward middle child of the Ada lineup. | RX 7900 XTX 24 GB AMD flagship; ROCm + Vulkan path. |
|---|---|---|
VRAM Largest model that fits. | Limited 16 GB GDDR6X. 70B impossible without offload; 22-24B Q4 fits. | Strong 24 GB GDDR6. 70B Q4 fits at 8K context; 32B FP16 fits comfortably. |
Memory bandwidth Decode speed. | Acceptable 736 GB/s. Decent but well behind the 7900 XTX. | Strong 960 GB/s. ~30% advantage on memory-bound decode. |
Compute (FP16) Prefill + matmul. | Strong ~52 TFLOPS FP16. Strong tensor cores; mature CUDA path. | Acceptable ~61 TFLOPS FP16 nominal but ROCm extracts less in practice. |
Software ecosystem Runtimes available. | Excellent Every production runtime. Day-zero new model wheels. | Acceptable llama.cpp ROCm/Vulkan + Ollama + vLLM ROCm. No SGLang / TensorRT-LLM / EXL2 GPU path. |
Day-zero new model support Time-to-supported. | Excellent Day-zero in most cases. | Acceptable ROCm wheels often lag CUDA wheels by days/weeks. |
Operator complexity Hours per month maintaining the rig. | Strong Standard NVIDIA driver flow. <1 h/month typical. | Limited Kernel pinning + ROCm version drift + occasional flash-attention regressions. |
Price (2026) Retail. | Acceptable $900-1,100. Awkward slot vs used 4090 above and 5080 sideways. | Excellent $700-900. Best $/GB-VRAM new in 2026. |
Power efficiency Perf-per-watt under load. | Strong 320W TDP. Strong perf-per-watt; runs cool. | Acceptable 355W TDP. Less efficient than Ada under sustained load. |
Tiers are qualitative editorial labels, not derived from a single benchmark. For tok/s and VRAM measurements on these cards, browse the corpus or request a benchmark.
Who should AVOID each option
Avoid the RTX 4080 Super
- If 24 GB VRAM matters for your common models
- If you can find a used 4090 at $1,400-1,700 in your market
- If price-per-VRAM-GB is the dominant axis
Avoid the RX 7900 XTX
- If your stack requires SGLang / TensorRT-LLM
- If you're not on Linux
- If kernel pinning + ROCm drift is unacceptable
Workload fit
RTX 4080 Super fits
- 13B-32B production serving
- vLLM / SGLang / TensorRT-LLM
- Day-zero new models
RX 7900 XTX fits
- 70B Q4 single-card
- Linux + llama.cpp / Ollama
- Best $/GB-VRAM new
Where to buy
Where to buy RTX 4080 Super
Editorial price range: $900-1,100 (2026; new + lightly used)
Affiliate links — no extra cost. Prices are editorial ranges, not real-time. Click through to verify.
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Editorial verdict
For Linux homelab operators on the llama.cpp / Ollama path, the 7900 XTX is the better pick. 24 GB at $800 beats 16 GB at $1,000 on every relevant axis except software ecosystem.
For anyone whose workflow touches vLLM in production, SGLang, TensorRT-LLM, or day-zero new models, pay the NVIDIA tax. The 4080 Super isn't perfectly priced but it slots into every CUDA pipeline immediately.
Strong consideration: the 7900 XTX vs used 4090 comparison eats both these cards. If used 4090 at $1,500-1,700 is in your market, that's the value pick. The 4080 Super is squeezed from above by the used 4090 and below by the 7900 XTX.
HonestyWhy benchmark numbers on this page might not reflect your real experience
- tok/s is not user experience. Humans read at ~10-15 tok/s — anything above that is buffer time, not perceived speed.
- Context length changes everything. A 70B Q4 model at 1024 tokens generates ~25 tok/s; the same model at 32K context drops to ~8-12 tok/s as KV cache fills.
- Quantization changes the conclusion. Q4_K_M vs Q5_K_M vs Q8 produce different speed AND different quality. A benchmark at one quant doesn't translate to another.
- Thermal throttling changes long sessions. The first 15 minutes of a benchmark see boost-clock peak; the next 4 hours see steady-state, which is 5-15% slower depending on case airflow.
- Driver and runtime versions silently shift winners. A 2024 benchmark on PyTorch 2.4 + CUDA 12.4 doesn't reflect 2026 reality on PyTorch 2.6 + CUDA 12.6. Discount benchmarks older than 6 months.
- Vendor and YouTuber benchmarks are cherry-picked. The standard 'Llama 3.1 70B Q4 at 1024 tokens' chart shows peak decode on a tiny prompt — exactly the conditions least representative of daily use.
- A 25-30% throughput gap between two cards rarely translates to a 25-30% experience gap. Both cards are fast enough; the differentiator is usually VRAM ceiling, not raw decode speed.
We try to surface these caveats where they apply. If a number on this page reads more confident than it should, please email us via contact. See also our methodology and editorial philosophy.
Don't see your specific workload?
The matrix above is editorial. If you want a measured tok/s number for a specific model + quant on either card, file a benchmark request — the community claims requests and reproduces them under our methodology checklist.