If you have been following the DeepSeek story, you know their 7B model consistently outperforms similarly-sized competitors on coding and mathematical reasoning benchmarks. The practical question for self-hosters is: what is the minimum GPU that makes it genuinely useful? After benchmarking on GigaGPU dedicated servers, we think the RTX 5060 might be that GPU.
Performance at a Glance
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 22.0 tok/s |
| Tokens/sec (batched, bs=8) | 28.6 tok/s |
| Per-token latency | 45.5 ms |
| Precision | INT4 |
| Quantisation | 4-bit GGUF Q4_K_M |
| Max context length | 4K |
| Performance rating | Good |
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.
Twenty-two tokens per second is a solid result for a 4-bit quantised model. At 45.5 ms per token, the response generation feels fluid and natural — no awkward pauses waiting for the next word. The 5060’s Ada Lovelace architecture brings meaningful improvements to integer compute, which specifically benefits INT4 quantised inference like this.
Memory Footprint
| Component | VRAM |
|---|---|
| Model weights (4-bit GGUF Q4_K_M) | 5.0 GB |
| KV cache + runtime | ~0.8 GB |
| Total RTX 5060 VRAM | 8 GB |
| Free headroom | ~3.0 GB |
Three gigabytes of free VRAM after loading the model gives you genuine flexibility. That is enough headroom to extend context slightly beyond the default 4K, or to handle a couple of overlapping requests without memory pressure. The DeepSeek 7B’s slightly leaner footprint compared to 8B models is a quiet advantage on memory-constrained cards like this.
Cost Per Token
| Cost Metric | Value |
|---|---|
| Server cost | £0.35/hr (£99/mo) |
| Cost per 1M tokens | £4.419 |
| Tokens per £1 | 226296 |
| Break-even vs API | ~1 req/day |
At £4.42 per million tokens, you are already well below typical API pricing. Batching brings it down to about £2.76, which makes the RTX 5060 one of the most cost-effective ways to run DeepSeek 7B. The £99/month flat rate means predictable costs regardless of how heavily you use it. Check our benchmark tool and cost calculator for cross-GPU comparisons.
The Sweet Spot for DeepSeek
The RTX 5060 handles DeepSeek 7B well enough for development, internal tools, and light customer-facing applications. Its strength is the combination of adequate speed, good memory headroom, and low monthly cost. If you specifically need DeepSeek for its reasoning strengths — coding assistants, math tutoring, structured data extraction — this is a sensible starting point.
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 99Read our DeepSeek hosting guide or best GPU for DeepSeek comparison. Compare with the LLaMA 3 8B on RTX 5060, and see all benchmarks.
DeepSeek 7B on RTX 5060
Reasoning-class AI at £99/mo. Fast enough for real work, cheap enough to run 24/7.
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