DeepSeek 7B on RTX 3090: Performance Benchmark & Cost, Category: Benchmarks, Slug: deepseek-7b-on-rtx-3090-benchmark, Excerpt: DeepSeek 7B benchmarked on RTX 3090: 44.0 tok/s at FP16, VRAM usage, cost per 1M tokens, and deployment configuration., Internal links: 9 –>

Here is a counterintuitive result: the RTX 3090, a GPU from 2020, runs DeepSeek 7B faster than the RTX 4060 Ti launched three years later. The reason is bandwidth. The 3090’s 384-bit memory bus delivers 936 GB/s, roughly double the 4060 Ti’s 288 GB/s — and LLM inference on a single stream is almost purely a memory-bandwidth exercise. We measured the results on GigaGPU dedicated servers.

Bandwidth-Driven Performance

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.

Forty-four tokens per second at FP16 is a 37% jump over the 4060 Ti’s 32 tok/s. Every token the model generates requires reading roughly 15 GB of weight data, so the wider memory bus translates almost linearly into higher throughput. The 22.7 ms per-token latency feels snappy and responsive, well suited for chat and interactive applications.

Comfortable Memory Situation

The 3090 gives DeepSeek 7B 9.3 GB of headroom — more than enough for 16K context windows and several concurrent users. This is where the 24 GB frame buffer proves its worth. You can run extended conversations, process longer documents, and serve multiple API consumers without worrying about memory pressure. Unlike the tight fit on the 4060 Ti, this setup has genuine production margins.

What You Pay

At £4.74 per million tokens, the per-token cost is slightly higher than the 4060 Ti despite the faster speed. The £149/month absolute cost is the reason — you are paying for the extra VRAM and bandwidth. Batched inference at roughly £2.96 per million tokens is competitive, especially considering you get 16K context and multi-user capacity included. See our full benchmark comparison.

A Serious Deployment Platform

The RTX 3090 running DeepSeek 7B is a strong pick for teams that need reliable, responsive inference with room to grow. The generous VRAM lets you experiment with longer contexts and higher batch sizes. It handles interactive chat, API serving, and document processing workloads with equal confidence. For even more speed, check what the newer RTX 5080 can do — but the 3090’s combination of bandwidth, memory, and price remains hard to beat.

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

Full setup in our DeepSeek hosting guide and GPU comparison. Compare against the LLaMA 3 8B on RTX 3090, or browse all benchmarks.