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

DeepSeek 7B and the RTX 5080 make for an interesting pairing. The Blackwell architecture GPU pushes this reasoning-focused model to 68 tokens per second — faster than the RTX 3090 by over 50%, though with less memory to work with. The question is whether raw speed or VRAM headroom matters more for your specific use case. We ran the benchmarks on GigaGPU dedicated servers to help you decide.

Blackwell Meets DeepSeek

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.

At 14.7 ms per token, DeepSeek 7B generates responses at a pace where individual words are essentially indistinguishable from streaming text. The batched throughput of 108.8 tok/s crosses the triple-digit barrier, making this a viable option for serving moderate API traffic with multiple simultaneous requests.

Tight but Workable Memory

The 5080’s 16 GB frame buffer fits DeepSeek 7B at FP16 with 1.3 GB to spare. That is tighter than the 3090’s luxurious 9.3 GB headroom, and it caps context at 8K. For DeepSeek’s core strengths — code generation, mathematical reasoning, structured output — this is usually fine since those tasks tend to involve shorter, more focused prompts. If you need 16K context for document-heavy workloads, the 3090 remains the better choice despite its lower throughput.

Speed-Adjusted Costs

The per-token cost of £3.88 slots neatly between the RTX 3090 (£4.74) and the flagship 5090 (£4.39). The 5080 actually delivers better cost efficiency than both because its Blackwell tensor cores are so much faster. With batching, you are looking at roughly £2.43 per million tokens. Use our benchmark tool and cost calculator to model your specific workload.

When Speed Trumps Context

Choose the RTX 5080 for DeepSeek 7B when your primary need is fast, responsive inference for coding assistants, chatbots, or any application where sub-second latency matters. Skip it if you need extended context windows for RAG pipelines or document analysis — for those, the 3090’s extra memory is worth the throughput trade-off.

Quick deploy:

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

Read our DeepSeek hosting guide and best GPU for DeepSeek. See the LLaMA 3 8B on RTX 5080 for comparison, or browse all benchmark results.