DeepSeek R1 Distill Llama 70B.
70B Llama distilled from DeepSeek R1's reasoning traces.
1× Nvidia L40S · $0.28/hr.
Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.
Cheapest hosted endpoints.
Speed across providers.
Tokens/sec and time-to-first-token measured against the same prompt template on each provider's API.
| Provider | Tokens/sec | TTFT | Total |
|---|---|---|---|
| OpenRouter | 47.0 | 6622 ms | 9189 ms |
Variants in the DeepSeek family.
DeepSeek V3
DeepSeek's flagship MoE — 671B total, 37B active, frontier-class.
DeepSeek R1
DeepSeek's reasoning model — RL-trained, frontier-class, MIT-licensed.
DeepSeek R1 Distill Qwen 32B
32B Qwen base distilled from DeepSeek R1.
DeepSeek R1 Distill Qwen 14B
14B distilled R1 — laptop-friendly reasoning.
DeepSeek R1 Distill Qwen 7B
7B distilled R1 — runs on any modern GPU.
DeepSeek R1 Distill Qwen 1.5B
Tiny distilled R1 — phone / browser deployable.
Frequently asked.
How do I run DeepSeek R1 Distill Llama 70B?
Where can I access DeepSeek R1 Distill Llama 70B?
How much does it cost to run DeepSeek R1 Distill Llama 70B?
Is DeepSeek R1 Distill Llama 70B open-source or proprietary?
Cheapest hardware per quantisation.
Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr refreshed hourly from each provider's API.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
192 GB | — | — | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
94 GB | — | — | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
48 GB | $0.28/hr | — | Compare → |
What it costs per month across providers.
Estimate your monthly bill for DeepSeek R1 Distill Llama 70B across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
Bill breakdown.
Rent the GPU instead of paying per token.
For an open-weights model like DeepSeek R1 Distill Llama 70B, you can rent a GPU and serve inference yourself. The math: cheapest GPU rental × 730 hours/month + your electricity rate × power draw.
Assumes the GPU runs 24/7 at ~85% utilisation. If your traffic is bursty, you'll pay less for the API and probably more for the GPU (idle hours still cost rental). The breakeven analysis lives on the Self-host vs API breakeven tool.
What it's best at.
Scores normalised against benchmark ceilings (100 = perfect). Coloured by tier — coral 80+ frontier, lavender 65+ strong, sage 50+ solid, slate below.
Published scores.
| Benchmark | Score | Source |
|---|---|---|
| MATH | 94.5 | official ↗ |
| MMLU-Pro | 70.0 | official ↗ |
| HumanEval | 83.0 | official ↗ |
About DeepSeek R1 Distill Llama 70B.
DeepSeek R1 Distill Llama 70B is a distilled student model — base architecture is Llama 3.3 70B, but post-trained on R1's reasoning chain-of-thought traces. Inherits most of R1's math and coding capability at 5% of the inference cost. Released MIT-licensed alongside the R1 paper. Fits comfortably on 2× H100 at FP16, or 1× H100 at INT4. Widely deployed as a cost-sensitive reasoning workhorse — much cheaper than full R1, much smarter than vanilla Llama 70B.