DeepSeek-R1-Distill-Llama-70B VRAM requirements

Short answer: DeepSeek-R1-Distill-Llama-70B is a dense 70B model based on Llama 3.3 70B Instruct, not the 671B-total/37B-active DeepSeek-R1 mixture-of-experts model. DeepSeek’s official repository displays about 141GB for the full-precision checkpoint. Raw planning arithmetic is about 35GB at 4-bit, 70GB at 8-bit and 140GB at 16-bit before artifact overhead, KV cache and runtime allocations.

Which DeepSeek model is this?

Model	Published identity	Memory-planning rule
DeepSeek-R1-Distill-Llama-70B	Dense model based on Llama 3.3 70B Instruct	Size the complete 70B stored-weight artifact
DeepSeek-R1-Distill-Qwen-32B	Dense model based on Qwen2.5-32B	Different 32B artifact and architecture
DeepSeek-R1	671B total / 37B activated MoE, 128K context	Active parameters do not replace total stored weights

Official checkpoint and weight-memory floor

The official Hugging Face repository displays a 141GB checkpoint split across 17 safetensors files. That is the repository’s full-precision artifact, not a quantized GGUF size or a measured peak allocation. For lower-precision planning, the transparent lower-bound formula is 70 billion parameters × bits per parameter ÷ 8.

Artifact or nominal precision	Size or raw floor	Hardware implication before overhead
Official full-precision repository	141GB displayed	Requires a large unified-memory or multi-device plan
4-bit raw floor	35GB / 32.6GiB	48GB is the first common one-device target
5-bit raw floor	43.75GB / 40.7GiB	Can be tight on 48GB after cache and buffers
6-bit raw floor	52.5GB / 48.9GiB	Points toward 64GB+
8-bit raw floor	70GB / 65.2GiB	Exceeds 64GB; can be tight on 80GB
16-bit raw floor	140GB / 130.4GiB	Consistent with the repository’s roughly 141GB display

Illustrative KV-cache payload at 8K, 32K and 128K

DeepSeek’s official configuration lists 131,072 maximum positions, 80 layers, eight KV heads and a 128-element head dimension. For one sequence with FP16 key/value entries:

2 × 80 layers × 8 KV heads × 128 head dimension × 2 bytes × token slots

Allocated token slots	Illustrative FP16 K+V payload	Combined with 4-bit raw floor
8,192	2.5GiB	About 35.1GiB before runtime overhead
32,768	10GiB	About 42.6GiB before runtime overhead
131,072	40GiB	About 72.6GiB before runtime overhead

Can it run on 24GB or 32GB?

Not as a complete nominal 4-bit 70B weight set inside either capacity: the raw floor is about 35GB before overhead. CPU offload, split placement or very low-bit community conversions can make some configurations load, but they change performance, quality or both. Do not treat “it launched” as proof that the intended context and output length fit.

Is 48GB enough?

Forty-eight gigabytes is the clearest one-device target for a compact 4-bit conversion and modest context. The official repository itself is not a 48GB artifact, and DeepSeek does not publish an official GGUF size table for this model. Inspect the exact community artifact, license, revision and runtime before planning headroom.

What do 64GB, 80GB and 96GB change?

• 64GB: more room for 4-bit/5-bit artifacts, cache and runtime state; still below the 8-bit raw floor.
• 80GB: can target 8-bit or longer-context 4-bit use, but weights plus a large cache can consume the margin.
• 96GB: more practical room for those workloads, though still below the official full-precision checkpoint.
• 128GB+: large unified-memory or multi-device territory; shared memory is not dedicated VRAM and multiple devices do not automatically pool memory.

Reasoning output changes the workload test

A reasoning-tuned model may produce long completions for some prompts. Maximum output length, stop behavior and prompt template therefore matter to latency and memory testing. DeepSeek recommends using its settings because the distilled models slightly change configs and tokenizers. Keep those settings explicit when comparing runtimes or hardware.

A reproducible validation checklist

1. Record the complete DeepSeek model name, artifact revision and quantization.
2. Verify that the runtime uses the model’s intended tokenizer and prompt settings.
3. Set context, cache precision, batch, concurrency and maximum output.
4. Add display use, buffers and other device allocations.
5. Measure peak device and host memory during the longest representative reasoning request.
6. Evaluate answer quality for the selected quantization.
7. Record prompt processing, generation speed and time to first token separately.

Sources and limits

• Official DeepSeek-R1-Distill-Llama-70B repository
• Official model configuration
• Official DeepSeek-R1 repository and model table
• Official Llama 3.3 70B base-model card
• HighVRAM methodology

Reviewed October 25, 2026. The 141GB repository size and architecture fields come from DeepSeek’s official pages. Weight and cache calculations are transparent planning arithmetic, not measured peak-memory claims. HighVRAM has not independently benchmarked every configuration. Capacity does not establish speed, reasoning quality or value.