AI Compute RadarModel · DeepSeek
DeepSeek-R1
685B MoE · Q4_K_M · 376.7 GB · open weights · curated for research · repo created 2025-01-20
On the radar
—
Heat Score · collecting
A score needs a week of daily readings; until then the model ranks after scored ones, never as a low score.
- Rank among scored models
- —
- Trending score · Hugging Face
- 127
- Downloads, rolling 30 days · Hugging Face
- 746,600
- Downloads vs. the earlier reading
- —
- Likes · Hugging Face
- 14,259
- API price per 1M tokens, in / out · OpenRouter
- $0.7 / $2.5
Measured signals as of 2026-09-18 06:00 UTC · How the Heat Score is made →
Downloads, daily readingsdaily last · UTC
2026-09-15 · 676K2026-09-18 · 747K
Heat is a composite of measured signals only: each component is the model's percentile among models with a full week of history — trending level (35%), 7-day download growth (30%), 7-day trending change (15%), 30-day downloads (15%) and Hub likes (5%). Missing components renormalize the weights and lower the shown confidence; nothing is guessed. Trending and downloads come from the Hugging Face Hub — the download counter is a rolling 30-day window, not unique users — and input pricing from OpenRouter (CC BY 4.0). The full formula, thresholds and flag rules are on the methodology page.
What it needs
◇ Estimated— Estimated: computed from our curated model and hardware catalog — not a live reading.Weights, context cache and runtime overhead at four reference contexts. The total is the model's own; what differs per machine is the usable memory it has to fit into.
| Context | Weights | Context cache | Overhead | Total |
|---|---|---|---|---|
| 8K | 376.7 | 0.5 | 12.2 | 389.4 GB |
| 32K | 376.7 | 2.1 | 12.9 | 391.7 GB |
| 64K | 376.7 | 4.3 | 13.9 | 394.9 GB |
| 128K | 376.7 | 8.6 | 15.9 | 401.2 GB |
All memory figures are estimates: measured quantized file size + computed context memory + runtime overhead, with a 12% safety margin on your hardware. Real usage varies with runtime version and settings.
Where it runs
◇ Estimated— Estimated: computed from our curated model and hardware catalog — not a live reading.Every tracked GPU and Mac at 8K and 32K context with a full-precision (f16) cache — the same engine as the finder and the GPU pages.
Runs comfortably (EXCELLENT or GOOD) on 0 of 14 tracked devices at 8K context.
| Device | 8K context | 32K context |
|---|---|---|
| RTX 407012 GB · 10.6 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 3060 12GB12 GB · 10.6 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 408016 GB · 14.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 508016 GB · 14.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 4060 Ti 16GB16 GB · 14.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 5060 Ti 16GB16 GB · 14.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 5070 Ti16 GB · 14.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 309024 GB · 21.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 409024 GB · 21.1 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| RTX 509032 GB · 28.2 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| Mac mini M4 Pro 64GB64 GB unified · 44.8 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| Mac Studio M4 Max 64GB64 GB unified · 44.8 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| Mac Studio M3 Ultra 96GB96 GB unified · 67.2 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
| NVIDIA DGX Spark128 GB unified · 112.6 GB usable | Not recommended389.4 GB | Not recommended391.7 GB |
GGUF is the quantized single-file format local runtimes load (Ollama, LM Studio, llama.cpp); our memory figures are measured from the linked GGUF file. The original repo holds full-precision safetensors — a much larger download meant for GPUs with far more memory.
Check it against your own machine →Or rent it
No measured rental class runs this model comfortably at 8K context.
Terms on this page
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