DeepSeek 7B on RTX 3050: Performance Benchmark & Cost, Category: Benchmarks, Slug: deepseek-7b-on-rtx-3050-benchmark, Excerpt: DeepSeek 7B benchmarked on RTX 3050: 10.0 tok/s at 4-bit GGUF Q4_K_M, VRAM usage, cost per 1M tokens, and deployment configuration., Internal links: 9 –>

DeepSeek’s 7B model has earned a reputation for punching above its weight on reasoning tasks, which makes it tempting to try on budget hardware. We loaded it onto the RTX 3050 — NVIDIA’s most affordable dedicated GPU at just 6 GB of VRAM — to find out whether DeepSeek 7B is practical on entry-level kit. The answer involves trade-offs, but there is good news for experimenters on GigaGPU dedicated servers.

Inference Speed at 4-Bit

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.

Ten tokens per second is noticeably faster than the 8 tok/s that LLaMA 3 8B achieves on identical hardware. DeepSeek 7B’s smaller parameter count (7B vs 8B) means the quantised model fits more comfortably in memory, leaving slightly more bandwidth for actual computation. It is still not quick — 100 ms per token is perceptible — but it maintains a usable conversational flow.

Fitting into 6 GB

DeepSeek 7B at 4-bit quantisation occupies 5.0 GB, leaving 1 GB of headroom — double what LLaMA 3 8B gets on the same card. That extra breathing room translates to marginally more stable operation. You are still limited to 4K context and should avoid pushing concurrent requests, but the model does not feel like it is gasping for memory the way the larger LLaMA does.

Token Economics on a Budget

At £6.94 per million tokens single-stream, this is not going to win any cost-efficiency awards. But it does not need to. At £49 per month flat, the RTX 3050 is cheap enough to run as a personal reasoning engine or dev sandbox. Batched inference drops the effective cost to roughly £4.34 per million tokens. Compare against other GPUs on our tokens-per-second benchmark.

Worth It for Experimentation

DeepSeek 7B on the RTX 3050 is best understood as a learning and prototyping setup. It is strong enough to evaluate DeepSeek’s reasoning capabilities, build proof-of-concept applications, and test prompt engineering — all without committing to more expensive hardware. For production, step up to the RTX 4060 for a clean 2x throughput improvement.

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

See our DeepSeek hosting guide and best GPU for DeepSeek comparison. Also check the LLaMA 3 8B on RTX 3050 for a head-to-head, or browse all benchmark results.