DeepSeek · DeepSeek R1

DeepSeek R1 Distill Qwen 32B

DeepSeek R1 Distill Qwen 32B is a 32B reasoning and chat model available through supported Badgr execution routes.

Last validated or meaningfully updated: 2026-07-27

ChatReasoningLong contextFine-tunable

Availability

Badgr AI API

Available through Badgr

Dedicated endpoint

Available through Badgr

Custom GPU deployment

Available through Badgr

Deployment profile

Architecture
Qwen2ForCausalLM
Licence
MIT
Minimum VRAM
~68GB
Context
128K tokens

Recommended: A100 80GB. VRAM is an estimate and increases with context, cache, concurrency, and runtime overhead.

Model identity

Updated
1 years ago
HuggingFace revision
711ad2ea6a

Popularity and trust

Downloads (last month)

689.9K

Likes

1.6K

Spaces using this model

100

Files and formats

Weight formats

Safetensors

Repository files

17

Chat template

Available

GPU deployment scenarios

Estimated from parameter count and quantisation. Will switch to “Verified by Badgr” once a real run backs a tier.

Testing

A100 80GB

Short context, low concurrency

Small production

H100 80GB

8K context, 1-4 concurrent requests

Higher throughput

H200 141GB

32K context, continuous batching

Large production

A100 80GB

Higher concurrency

Estimated — multimodal inputs, long context, and concurrency all increase real VRAM use beyond this estimate.

Also runs well on H100 80GB, H200 141GB. Available in United States, Europe, Asia Pacific.

Serving compatibility

RuntimeStatusNotes
vLLMEstimatedUsed for Badgr dedicated endpoints
TransformersDeclared by sourceBasic fallback
llama.cppRequires GGUF conversionNo GGUF weights found in repo
SGLangUnknownNot evaluated
TensorRT-LLMUnknownConversion may be required
TGIUnknownNot evaluated

Call this model through Badgr

curl https://api.aibadgr.com/v1/chat/completions \
  -H "Authorization: Bearer $BADGR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B","messages":[{"role":"user","content":"Hello"}]}'

Ready-to-run Badgr configurations

Quick test

badgr serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --max-cost 2

Validated dedicated endpoint command

badgr serve deepseek-ai/DeepSeek-R1-Distill-Qwen-32B --gpu A100 80GB --max-cost 10

Advanced configuration (defaults to automatic)

  • Runtime
  • Quantisation
  • GPU and GPU count
  • Context length
  • Maximum concurrency
  • Region
  • Maximum hourly spend
  • Persistent or capped runtime

Estimated pricing

Provider estimate: cold start

3–8 min

Provider estimate: endpoint cost

$1.35 – $1.35/hr

Idle cost

$0 when stopped

Per-token throughput cost is not shown here: Badgr has not benchmarked this model yet, and this page does not display figures it cannot back with real data.

Recommended Badgr routes

A100 80GB · United States

Available

from $1.35/hr

Best for

LoRA Training, vLLM Endpoint, Qwen 2.5 32B Instruct

Startup: 3–8 min

Reliability: High

Last checked: just now

A100 80GB · United States

Available

from $1.35/hr

Best for

LoRA Training, vLLM Endpoint, Qwen 2.5 32B Instruct

Startup: 3–8 min

Reliability: High

Last checked: just now

A100 80GB · United States

Available

from $1.35/hr

Best for

LoRA Training, vLLM Endpoint, Qwen 2.5 32B Instruct

Startup: 3–8 min

Reliability: Good

Last checked: just now

A100 80GB · United States

Available

from $1.35/hr

Best for

LoRA Training, vLLM Endpoint, Qwen 2.5 32B Instruct

Startup: 3–8 min

Reliability: High

Last checked: just now

A100 80GB · Europe

Available

from $1.35/hr

Best for

LoRA Training, vLLM Endpoint, Qwen 2.5 32B Instruct

Startup: 3–8 min

Reliability: Good

Last checked: just now

A100 80GB · Europe

Available

from $1.35/hr

Best for

LoRA Training, vLLM Endpoint, Qwen 2.5 32B Instruct

Startup: 3–8 min

Reliability: High

Last checked: just now

Known limitations

  • Memory use increases with context length and concurrency.

Related troubleshooting