DeepSeek-R1-Distill-Qwen-32B VRAM requirements

Short answer: DeepSeek’s official DeepSeek-R1-Distill-Qwen-32B repository displays about 65.5GB for its full-precision checkpoint. Raw 32B-label arithmetic is about 16GB at 4-bit, 32GB at 8-bit and 64GB at 16-bit before artifact overhead, KV cache and runtime allocations. A compact 4-bit conversion can target 24GB, but long context quickly changes the answer.

Which DeepSeek model is this?

Model	Published identity	Memory-planning rule
DeepSeek-R1-Distill-Qwen-32B	Dense model based on Qwen2.5-32B	Size the complete 32B stored-weight artifact
DeepSeek-R1-Distill-Llama-70B	Dense model based on Llama 3.3 70B Instruct	Different 70B capacity tier
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 repository displays 65.5GB across eight safetensors shards. That is the full-precision checkpoint, not an official GGUF table or a measured peak allocation. Lower-precision values below use the transparent formula 32 billion parameters × bits per parameter ÷ 8.

Artifact or nominal precision	Size or raw floor	Hardware implication before overhead
Official full-precision repository	65.5GB displayed	Targets 80GB+ or deliberate multi-device/unified-memory placement
4-bit raw floor	16GB / 14.9GiB	Can target 24GB with exact-artifact and workload checks
5-bit raw floor	20GB / 18.6GiB	Leaves limited nominal room on 24GB
6-bit raw floor	24GB / 22.4GiB	Already consumes the 24GB label before overhead
8-bit raw floor	32GB / 29.8GiB	Exceeds a 32GB label after typical cache and runtime allocations
16-bit raw floor	64GB / 59.6GiB	Consistent with the repository’s roughly 65.5GB display

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

DeepSeek’s official configuration lists 131,072 maximum positions, 64 layers, eight KV heads, a 5,120 hidden dimension and 40 attention heads—giving a 128-element head dimension. For one sequence with FP16 key/value entries:

2 × 64 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	2GiB	About 16.9GiB before runtime overhead
32,768	8GiB	About 22.9GiB before runtime overhead
131,072	32GiB	About 46.9GiB before runtime overhead

Can DeepSeek-R1-Distill-Qwen-32B run on 16GB?

A nominal 4-bit raw floor is already 16GB before file-format overhead, cache and runtime state, so 16GB is not a comfortable complete-device target. Very low-bit conversions or CPU offload can make some configurations load, but they change output quality or performance. Treat those as separate artifacts, not proof that every “4-bit 32B” workload fits.

Is 24GB enough?

Twenty-four gigabytes is the first common target for a compact 4-bit conversion and modest context. A nominal 5-bit floor leaves less room, while the 6-bit raw floor reaches 24GB before overhead. At 32K, the illustrative FP16 cache payload alone is about 8GiB, so context, cache precision and concurrency are decisive.

What do 32GB and 48GB change?

Thirty-two gigabytes gives compact 4-bit/5-bit artifacts more working room, but it is not a complete nominal 8-bit tier: the raw 8-bit floor already equals 32GB. Forty-eight gigabytes adds meaningful room for longer context, larger quantizations or concurrency, yet a full 128K FP16 cache plus the 4-bit raw floor totals about 46.9GiB before runtime overhead—already more than a 48GB decimal label can provide.

Reasoning output changes the workload test

Reasoning-tuned models can produce long completions. Maximum output length, stop behavior, prompt template and generation settings affect latency and memory use. DeepSeek notes that its distilled models slightly change configs and tokenizers and recommends using its settings. Keep those fields explicit in comparisons.

A reproducible validation checklist

1. Record the complete model name, artifact revision and quantization.
2. Verify the intended tokenizer, prompt template and generation 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 output quality for the selected quantization.
7. Record prompt processing, generation speed and time to first token separately.

Sources and limits

• Official DeepSeek-R1-Distill-Qwen-32B repository
• Official model configuration
• Official DeepSeek-R1 repository and model table
• HighVRAM methodology

Reviewed October 25, 2026. The 65.5GB repository display 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.