QwQ 32B Q5 VRAM Requirement and Ollama Model Size
QwQ 32B Q5 needs 20GB minimum VRAM, 30GB optimal VRAM, and 64GB+ system RAM for a safer Ollama run. Popular Ollama model family: QwQ. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | QwQ |
|---|---|
| Scenario | reasoning |
| License scope | open-source |
| Quantization | Q5 |
| VRAM minimum | 20GB |
| VRAM optimal | 30GB |
| Best local GPU | RTX 6000 Ada 48GB |
| Cloud fallback | A100 80GB |
| Updated | 2026-02-24 |
| Data status | Verified by Real Hardware |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | qwq:32b |
| Category | reasoning |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 9.9 |
| RTX 4090 24GB | 13.4 |
| A100 80GB | 23.8 |
Quick Answers
- How much VRAM does QwQ 32B Q5 need?
- QwQ 32B Q5 needs about 20GB minimum VRAM and 30GB optimal VRAM for a safer local run target.
- Can QwQ 32B Q5 run on an RTX 3090 24GB?
- It may load on an RTX 3090 24GB because the minimum estimate is 20GB, but the safer target is 30GB. Expect tighter context or lower throughput.
- What is the Ollama command for QwQ 32B Q5?
- Use ollama run qwq:32b. Check the Ollama tag qwq:32b before running.
- What is the approximate Ollama model size for QwQ 32B Q5?
- QwQ 32B Q5 uses the qwq:32b Ollama tag. Treat the Q5 32 profile as smaller than the runtime VRAM budget; use 20GB minimum VRAM and 30GB optimal VRAM as the safer sizing numbers.
- How much system RAM does QwQ 32B Q5 need in Ollama?
- Plan for at least 64GB system RAM alongside 20GB minimum VRAM and 30GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for QwQ 32B Q5?
- QwQ 32B Q5 should be sized by GPU VRAM first: 20GB minimum and 30GB optimal. System RAM should be at least 64GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is QwQ 32B Q5 VRAM usage on an RTX 3090?
- QwQ 32B Q5 is borderline for an RTX 3090 24GB target. The page estimates 20GB minimum and 30GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the qwq:32b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: QwQ 32B Q5 (Q5, 20GB min/30GB optimal); QwQ 32B Q4 (Q4, 18GB min/28GB optimal); QwQ 32B Q8 (Q8, 24GB min/34GB optimal); QwQ 32B FP16 (FP16, 30GB min/42GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is QwQ 32B Q5 benchmark data measured or estimated?
- QwQ 32B Q5 has a verified RTX 3090 result of 28.85 tokens/s from LocalVRAM benchmark data.
Ollama Model Size and RAM Notes
| Ollama tag | qwq:32b |
|---|---|
| Run command | ollama run qwq:32b |
| Model size guidance | Q5 quantization, 32 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 20GB minimum, 30GB optimal |
| System RAM planning | 64GB or more when running this tag locally with Ollama |
Download size alone is not a safe runtime budget. Runtime memory also includes KV cache, context length, and GPU layer placement.
Ollama RAM vs VRAM Requirements
| GPU VRAM usage estimate | 20GB minimum / 30GB optimal |
|---|---|
| System RAM requirement | 64GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Borderline |
| CPU spill risk | High without larger GPU or cloud |
| Context warning | Longer context increases KV cache memory, so real VRAM usage can exceed short-prompt estimates. |
For Ollama, VRAM is the first fit check. System RAM matters when layers spill to CPU, when context grows, or when the local app stack runs beside the model.
Available Tag Variants and VRAM Targets
| Profile | Quantization | VRAM target | Ollama tag |
|---|---|---|---|
| QwQ 32B Q5 | Q5 | 20GB min / 30GB optimal | qwq:32b |
| QwQ 32B Q4 | Q4 | 18GB min / 28GB optimal | qwq:32b |
| QwQ 32B Q8 | Q8 | 24GB min / 34GB optimal | qwq:32b |
| QwQ 32B FP16 | FP16 | 30GB min / 42GB optimal | qwq:32b |
Ollama library tags often group multiple quantization choices under one model family. LocalVRAM separates them into VRAM profiles so you can pick the right local target.
Real Hardware Benchmark (RTX 3090)
| Tokens/s | 28.85 |
|---|---|
| Latency | 3770 ms |
| Prompt tokens | 29 |
| Eval tokens | 96 |
| Test time | 2026-08-19T03:08:42Z |
| GPU model | NVIDIA GeForce RTX 3090 |
Verified by real hardware.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for QwQ 32B Q5
- Local run: RTX 3090 (24GB) (Check latest deal) for around 28.85 tok/s on this profile.
- Cloud run: RunPod A100 80GB , about 0.8x the local 3090 speed anchor.
- Alternative cloud: Vast.ai options for flexible spot pricing.
Local vs Cloud Cost Hint
| Mode | 40h / month | 120h / month |
|---|---|---|
| Local power only (3090 baseline) | $2.24 | $6.72 |
| A100 80GB | $78 | $234 |
Related Model Profiles
- QwQ 32B Q4 18GB min, 28GB optimal
- QwQ 32B Q8 24GB min, 34GB optimal
- QwQ 32B FP16 30GB min, 42GB optimal
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