Quantisation is a necessary evil on smaller GPUs, but it does come with a cost — subtle accuracy drops on complex reasoning tasks where DeepSeek 7B normally excels. The RTX 4060 Ti eliminates that compromise entirely. With 16 GB of VRAM, it runs DeepSeek 7B at full FP16 precision while delivering 32 tok/s — a genuine upgrade in both quality and speed over the quantised 4060 setup. Here are the details from our tests on GigaGPU dedicated servers.
FP16 Throughput
| Metric | Value |
|---|---|
| Tokens/sec (single stream) | 32.0 tok/s |
| Tokens/sec (batched, bs=8) | 51.2 tok/s |
| Per-token latency | 31.2 ms |
| Precision | FP16 |
| Quantisation | FP16 |
| Max context length | 8K |
| 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.
Thirty-two tokens per second at native precision is fast enough that responses appear almost as quickly as you can read them. The 31.2 ms per-token latency puts this firmly in the interactive category. What stands out is the batched throughput: 51.2 tok/s means you can run a small API serving 3-4 concurrent users without any one of them feeling the slowdown.
Memory Allocation
| Component | VRAM |
|---|---|
| Model weights (FP16) | 14.7 GB |
| KV cache + runtime | ~2.2 GB |
| Total RTX 4060 Ti VRAM | 16 GB |
| Free headroom | ~1.3 GB |
DeepSeek 7B in FP16 is a tighter fit than you might expect on 16 GB. The 14.7 GB model weight plus 2.2 GB for the KV cache leaves only 1.3 GB free. That is enough for stable single-user operation with 8K context, but do not try to push much further. If you need extended context or more concurrent users, the RTX 3090 with its 24 GB is the next logical step.
Economics of Full Precision
| Cost Metric | Value |
|---|---|
| Server cost | £0.50/hr (£99/mo) |
| Cost per 1M tokens | £4.340 |
| Tokens per £1 | 230415 |
| Break-even vs API | ~1 req/day |
You pay £30 more per month than the RTX 4060, but you get 45% more throughput and full precision. On a per-token basis, the 4060 Ti is actually cheaper at £4.34 versus £4.42 on the 4060. With batching, it drops to approximately £2.71 per million tokens. For an even deeper cost analysis, see our benchmark comparison.
The Full-Precision Advantage
If you chose DeepSeek specifically for its reasoning capabilities, running it at FP16 preserves the precision that makes it special. This matters most for code generation, mathematical problem solving, and structured output tasks. The 4060 Ti gives you that quality without breaking the bank — a strong middle-ground option.
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 99More in our DeepSeek hosting guide and best GPU for DeepSeek. See the LLaMA 3 8B on RTX 4060 Ti or browse all benchmarks.
DeepSeek 7B Without Compromise
Full FP16 precision at 32 tok/s. RTX 4060 Ti, UK datacenter, root access.
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