NVIDIA RTX 5090 vs. AMD RX 9070 XT: Which GPU Actually Makes Sense for Local LLM Inference in 2025?

The Local Inference GPU Market Just Got Complicated

For the past three years, the answer to "which GPU for local LLM inference" was almost embarrassingly simple: buy the most NVIDIA card you can afford, prioritize VRAM, accept the CUDA tax, and move on. That calculus is no longer clean.

NVIDIA's RTX 5090↗ launched in January 2025 with 32 GB of GDDR7 and a memory bandwidth figure — 1,792 GB/s — that genuinely redefines what a consumer desktop card can do for transformer workloads. AMD, meanwhile, shipped the RX 9070 XT↗ in March 2025 at roughly one-third the price, with 16 GB of GDDR6 and a ROCm software stack that has quietly matured into something usable. Neither card is obviously correct for every buyer.

This guide is for technically fluent readers running llama.cpp, Ollama, vLLM, or ExLlamaV2 locally — not gamers who occasionally run a chatbot. The analysis below is grounded in measured throughput, VRAM constraints, and honest software-ecosystem accounting.

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Spec Comparison: What the Numbers Actually Say

NVIDIA GeForce RTX 5090

• Architecture: Blackwell (GB202)
• VRAM: 32 GB GDDR7
• Memory Bandwidth: 1,792 GB/s
• TDP: 575 W
• CUDA Cores: 21,760
• Launch MSRP: $1,999 (street price routinely $2,400–$2,800 due to constrained supply)
• NVLink: Not supported on consumer SKU
• Driver ecosystem: CUDA 12.8, full llama.cpp CUDA backend, vLLM native support

AMD Radeon RX 9070 XT

• Architecture: RDNA 4 (Navi 48)
• VRAM: 16 GB GDDR6
• Memory Bandwidth: 644 GB/s
• TDP: 304 W
• Compute Units: 64
• Launch MSRP: $599 (street price $620–$680 as of April 2025)
• ROCm support: ROCm 6.2 with HIP backend; llama.cpp Vulkan/HIP path functional
• Driver ecosystem: Improving but still fragmented on Linux for inference stacks

Memory bandwidth is the single most important hardware variable for LLM inference on consumer GPUs. Transformers at inference time are memory-bandwidth-bound, not compute-bound — the arithmetic intensity of autoregressive decoding is far too low to saturate modern shader arrays. This is why a 575 W GPU with 1,792 GB/s will produce dramatically faster token generation than a 304 W GPU at 644 GB/s, independent of shader count.

The RTX 5090's bandwidth advantage is 2.78× over the RX 9070 XT. In practice, for a quantized Llama 3 70B Q4_K_M model (approximately 39 GB — which already exceeds both cards' VRAM and requires CPU offloading or a multi-GPU setup), the bandwidth gap translates almost linearly into token generation speed differences when layers are fully resident on GPU.

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VRAM: The Hard Constraint

This is where the conversation bifurcates cleanly by use-case.

16 GB (RX 9070 XT) fits: - Llama 3 8B in full BF16 (~16 GB — tight, context-dependent) - Llama 3 8B Q8_0 (~8.5 GB) with room for context - Mistral 7B / Qwen 2.5 7B variants comfortably - Llama 3 70B only with aggressive quantization (Q2_K, ~26 GB) — requires CPU offloading, kills throughput - Gemma 3 12B Q4_K_M (~7.5 GB) — fits cleanly

32 GB (RTX 5090) fits: - Llama 3 70B Q4_K_M (~39 GB) — still requires offloading; 32 GB is not enough - Llama 3 70B Q3_K_S (~29 GB) — fits entirely on-card, this is the sweet spot - Qwen 2.5 72B Q3_K_M (~30 GB) — fits with tight margin - Llama 3 8B BF16 — trivially fits, bandwidth makes it extremely fast - Mistral Large 2 (123B) — requires multi-GPU or CPU offload regardless

The uncomfortable truth: neither card fully contains a 70B model at Q4 quality. If your target is running frontier-class open-weight models at their best quantization tier without offloading, you are still looking at dual-GPU setups or the NVIDIA RTX 6000 Ada↗ (48 GB, ~$6,800) in the professional segment.

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Real Throughput: Token Generation Benchmarks

Benchmark data sourced from llama.cpp community performance tracking↗ and independent measurements published by Tom's Hardware↗ and TechPowerUp↗ in Q1 2025.

Llama 3 8B Q4_K_M — tokens/second (single GPU, no offload):

• RTX 5090: ~185–210 t/s
• RTX 4090 (reference): ~110–125 t/s
• RX 9070 XT (HIP/Vulkan): ~68–82 t/s
• RTX 4070 Ti Super: ~75–88 t/s

Llama 3 70B Q3_K_S — tokens/second (fully on-GPU where VRAM allows):

• RTX 5090: ~28–34 t/s (fits on card)
• RX 9070 XT: not measurable fully on-card; with CPU offload drops to ~4–8 t/s
• RTX 4090: ~18–22 t/s (fits on card with Q3_K_S at ~29 GB — marginal)

These figures make the use-case split stark. For 7B–13B models, the RX 9070 XT is usable — not fast, but functional. For 70B-class models, the performance gap becomes a workflow question: do you want 30 t/s or 5 t/s?

The RX 9070 XT running Llama 3 8B at 75 tokens/second is fast enough for interactive use. The same card attempting Llama 3 70B with CPU offloading produces output at roughly reading speed — technically functional, practically frustrating for iterative development workflows.

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Software Ecosystem: The AMD Caveat That Still Matters

ROCm has improved substantially. ROCm 6.2↗, released in late 2024, brought better HIP kernel coverage and improved llama.cpp integration. The Vulkan compute path in llama.cpp is a legitimate fallback that works on RDNA 4 without ROCm installation overhead.

However, the ecosystem gaps are real:

• vLLM has experimental ROCm support but production deployments on AMD remain uncommon and less documented
• ExLlamaV2 — arguably the fastest local inference engine for NVIDIA — has no mature AMD path
• Transformers + bitsandbytes quantization has limited ROCm compatibility
• Windows ROCm support for the RX 9070 XT is nascent; Linux is the only reliable platform for inference workloads
• CUDA's tooling depth — profiling, kernel debugging, custom attention implementations — has no equivalent on the AMD consumer stack

For a developer building inference pipelines, testing custom kernels, or integrating with the broader ML ecosystem, CUDA's network effects are not marketing — they are real productivity costs measured in hours of debugging.

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Power and Thermal Considerations

The RTX 5090's 575 W TDP is not a casual number. A workstation running a 5090 alongside a modern CPU (65–125 W) will require an 850 W–1000 W PSU at minimum, with proper airflow for sustained inference loads. In Tokyo's summer ambient temperatures, thermal management in a home office or small studio becomes a genuine concern — not hypothetical.

The RX 9070 XT at 304 W is far more tractable. A quality 750 W PSU handles the full system comfortably, heat output is manageable in a mid-tower, and electricity costs — increasingly relevant for always-on inference servers — are meaningfully lower. Japan's residential electricity rates↗ average around ¥31–36/kWh in 2025; running a 5090 system at sustained 700 W load for 8 hours daily adds roughly ¥500–600 to monthly bills versus a 9070 XT system.

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Buying Recommendations by Use-Case

Budget-Conscious Developers: 7B–13B Model Focus → RX 9070 XT at ¥95,000–¥105,000 (~$620–$680)

• Fits Llama 3 8B, Gemma 3 12B, Mistral 7B cleanly
• Acceptable token throughput for interactive use
• Low power draw suits home office setups
• Acceptable if your stack is llama.cpp or Ollama on Linux
• Not recommended if you need ExLlamaV2, vLLM production, or Windows-first workflow

Serious Local Inference — 70B Model Work → RTX 5090 at ¥310,000–¥420,000 (~$2,000–$2,800)

• Only consumer card that makes 70B inference practically usable at Q3–Q4 quality
• 1,792 GB/s bandwidth delivers genuinely fast 8B inference for rapid iteration
• Full CUDA ecosystem access
• Justified if 70B model quality is a workflow requirement, not a curiosity
• Power and cost are real — budget accordingly

The Overlooked Middle Ground → RTX 4090 used market at ¥160,000–¥200,000 (~$1,050–$1,300)

• 24 GB VRAM, 1,008 GB/s bandwidth
• Fits 70B at Q2_K; fits 34B models comfortably at Q4
• Full CUDA ecosystem, mature driver support
• Significantly better value than RTX 5090 for most local inference workflows
• Worth serious consideration before committing to 5090 pricing

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Final Verdict

The RTX 5090 is the best consumer GPU for local LLM inference that exists today — its bandwidth advantage is real, its VRAM headroom is the widest available at consumer pricing, and the CUDA ecosystem remains unmatched. But $2,000–$2,800 for a card that still cannot fully contain a 70B Q4 model is a genuinely uncomfortable value proposition.

The RX 9070 XT is not the right card for serious 70B work. It is, however, a capable and power-efficient card for developers whose target models are 7B–13B, whose stack is llama.cpp-compatible, and who work primarily on Linux. At one-third the price, it earns its place in that specific use-case.

For most technically serious buyers who want 70B capability without the 5090's price: the used RTX 4090 market remains the most rational choice in April 2025. The 5090 is worth the premium only if bandwidth-limited throughput on 8B models is a daily workflow bottleneck, or if you are running inference as a service and billing for latency.

The numbers, as always, make the argument.