Mistral 7B on RTX 5080: Performance Benchmark & Cost, Category: Benchmarks, Slug: mistral-7b-on-rtx-5080-benchmark, Excerpt: Mistral 7B benchmarked on RTX 5080: 68.0 tok/s at FP16, VRAM usage, cost per 1M tokens, and deployment configuration., Internal links: 9 –>

Sixty-eight tokens per second from a single GPU changes the maths on what a dedicated server can handle. That is what the RTX 5080 delivers running Mistral 7B at FP16, and it means a single machine can serve the kind of latency-sensitive workloads that previously required cloud API subscriptions. We tested this setup on GigaGPU dedicated servers to see where Blackwell architecture takes Mistral inference.

Blackwell-Powered Speed

Benchmark conditions: single-stream generation, 512-token prompt, 256-token completion, llama.cpp or vLLM backend. GGUF Q4_K_M via llama.cpp or vLLM FP16.

The 5080 pushes 54% more tokens per second than the RTX 3090 (68 vs 44). Mistral’s efficient architecture pairs well with Blackwell’s improved tensor cores — the grouped-query attention heads and sliding window mechanism translate into less memory traffic per token, which the 5080’s high-bandwidth memory subsystem exploits effectively. Batched at 108.8 tok/s, this GPU crosses into triple-digit territory.

The Memory Constraint

The trade-off for all that speed is familiar: 16 GB minus the model leaves only 1.3 GB free. You get 8K context and single-user operation comfortably, but multi-user serving requires careful KV cache management. Mistral’s sliding window attention helps here — it discards older context beyond the window boundary, naturally limiting memory growth. Still, if your use case demands extended context or high concurrency, the RTX 3090’s 9.3 GB headroom or the 5090’s 17.3 GB may serve you better.

Cost Efficiency Breakdown

The £3.88 per-token cost is actually the best in the Mistral lineup for single-stream — cheaper than the 3090, the 4060 Ti, and even the flagship 5090. Blackwell’s efficiency advantage shows up directly in the economics. Batching drops you to approximately £2.43 per million tokens. Our tokens-per-second benchmark lays out the full comparison across GPUs.

Speed-Optimised Deployment

The RTX 5080 is the Mistral 7B choice for teams that prioritise response speed over context length. Customer-facing chatbots, code completion services, and real-time classification tasks all benefit from the 14.7 ms latency. If you need both speed and long context, consider running 4-bit quantisation on the 5080 instead — it frees up roughly 10 GB of VRAM while keeping throughput well above 60 tok/s.

Quick deploy:

docker run --gpus all -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server -m /models/mistral-7b.Q4_K_M.gguf --host 0.0.0.0 --port 8080 -ngl 99

Full details in our Mistral hosting guide and GPU comparison. See LLaMA 3 8B on RTX 5080 or check all benchmarks.