Table of Contents
DeepSeek R1 Distill Benchmark Overview
DeepSeek R1 Distill is a distilled reasoning model that brings chain-of-thought capabilities into a compact 7B-parameter package. It excels at maths, logic, and step-by-step problem solving while remaining deployable on a single dedicated GPU server. We benchmark inference speed across six GPUs to guide your hardware selection.
All tests were conducted on GigaGPU servers with vLLM, using a 512-token input and 256-token output generation. DeepSeek R1 Distill 7B requires approximately 14 GB at FP16. For our full testing methodology, refer to the tokens per second benchmark page.
Tokens/sec Results by GPU
| GPU | VRAM | R1 Distill 7B FP16 (tok/s) | Notes |
|---|---|---|---|
| RTX 3050 | 6 GB | N/A | Insufficient VRAM for FP16 |
| RTX 4060 | 8 GB | N/A | Insufficient VRAM for FP16 |
| RTX 4060 Ti | 16 GB | 30 tok/s | Comfortable fit |
| RTX 3090 | 24 GB | 42 tok/s | Plenty of headroom |
| RTX 5080 | 16 GB | 66 tok/s | Next-gen advantage |
| RTX 5090 | 32 GB | 92 tok/s | Top single-GPU speed |
DeepSeek R1 Distill performs similarly to other 7B models at FP16. The RTX 5090 at 92 tok/s provides excellent latency for interactive reasoning applications.
FP16 vs INT8 vs INT4 Comparison
Quantisation opens R1 Distill to budget GPUs while also boosting throughput on higher-end cards. See our quantisation speed comparison for a detailed analysis of precision trade-offs.
| GPU | FP16 (tok/s) | INT8 (tok/s) | INT4 (tok/s) |
|---|---|---|---|
| RTX 3050 (6 GB) | N/A | N/A | 9 tok/s |
| RTX 4060 (8 GB) | N/A | 17 tok/s | 22 tok/s |
| RTX 4060 Ti (16 GB) | 30 | 36 tok/s | 44 tok/s |
| RTX 3090 (24 GB) | 42 | 50 tok/s | 59 tok/s |
| RTX 5080 (16 GB) | 66 | 76 tok/s | 89 tok/s |
| RTX 5090 (32 GB) | 92 | 108 tok/s | 126 tok/s |
INT4 delivers 35-47% more tokens per second than FP16, with reasoning tasks showing only minor quality impacts at 4-bit precision. For maths-heavy workloads where accuracy is paramount, INT8 offers a solid middle ground.
Cost Efficiency Analysis
| GPU | FP16 tok/s | Approx. Monthly Cost | tok/s per Pound |
|---|---|---|---|
| RTX 4060 Ti | 30 | ~£75 | 0.40 |
| RTX 3090 | 42 | ~£110 | 0.38 |
| RTX 5080 | 66 | ~£160 | 0.41 |
| RTX 5090 | 92 | ~£250 | 0.37 |
The RTX 5080 narrowly leads on cost efficiency at FP16, with the RTX 4060 Ti close behind. Check our best GPU for DeepSeek guide for more detailed recommendations.
GPU Recommendations
- Best budget: RTX 4060 Ti — 30 tok/s FP16 handles development and low-traffic reasoning APIs well.
- Best value: RTX 5080 — highest cost efficiency with strong 66 tok/s at FP16.
- Best performance: RTX 5090 — 92 tok/s for real-time reasoning chatbot deployments.
- Budget INT4: RTX 4060 — 22 tok/s at INT4 for experimentation and prototyping.
Compare DeepSeek R1 Distill with other 7B models in our Qwen 2.5 7B benchmark or the Gemma 2 9B results. Browse all results in the Benchmarks category.
Conclusion
DeepSeek R1 Distill brings powerful reasoning to a deployable 7B model. With FP16 running comfortably on 16 GB GPUs and INT4 making even 6-8 GB cards viable, it is one of the most accessible reasoning models available for dedicated server deployment.
Deploy DeepSeek R1 Distill on Dedicated Hardware
Bare-metal GPU servers with full root access, optimised for LLM reasoning workloads.
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