Qwen2.5 72B FP16 VRAM Requirement and Ollama Model Size
Qwen2.5 72B FP16 needs 50GB minimum VRAM, 62GB optimal VRAM, and 128GB+ system RAM for a safer Ollama run. Popular Ollama model family: Qwen2.5. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | Qwen2.5 |
|---|---|
| Scenario | coding |
| License scope | open-source |
| Quantization | FP16 |
| VRAM minimum | 50GB |
| VRAM optimal | 62GB |
| Best local GPU | Dual RTX 4090 (model parallel) |
| Cloud fallback | A100 80GB |
| Updated | 2026-02-24 |
| Data status | Estimated baseline (pending measurement) |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | qwen2.5:72b |
| Category | coding |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 3.7 |
| RTX 4090 24GB | 5 |
| A100 80GB | 8.9 |
Quick Answers
- How much VRAM does Qwen2.5 72B FP16 need?
- Qwen2.5 72B FP16 needs about 50GB minimum VRAM and 62GB optimal VRAM for a safer local run target.
- Can Qwen2.5 72B FP16 run on an RTX 3090 24GB?
- Not as a comfortable local target. Qwen2.5 72B FP16 needs about 50GB minimum and 62GB optimal VRAM, so cloud or larger local GPUs are safer.
- What is the Ollama command for Qwen2.5 72B FP16?
- Use ollama run qwen2.5:72b. Check the Ollama tag qwen2.5:72b before running.
- What is the approximate Ollama model size for Qwen2.5 72B FP16?
- Qwen2.5 72B FP16 uses the qwen2.5:72b Ollama tag. Treat the FP16 72 profile as smaller than the runtime VRAM budget; use 50GB minimum VRAM and 62GB optimal VRAM as the safer sizing numbers.
- How much system RAM does Qwen2.5 72B FP16 need in Ollama?
- Plan for at least 128GB system RAM alongside 50GB minimum VRAM and 62GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for Qwen2.5 72B FP16?
- Qwen2.5 72B FP16 should be sized by GPU VRAM first: 50GB minimum and 62GB optimal. System RAM should be at least 128GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is Qwen2.5 72B FP16 VRAM usage on an RTX 3090?
- Qwen2.5 72B FP16 is not recommended for an RTX 3090 24GB target. The page estimates 50GB minimum and 62GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the qwen2.5:72b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: Qwen2.5 72B FP16 (FP16, 50GB min/62GB optimal); Qwen2.5 72B Q4 (Q4, 38GB min/48GB optimal); Qwen2.5 72B Q5 (Q5, 40GB min/50GB optimal); Qwen2.5 72B Q8 (Q8, 44GB min/54GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is Qwen2.5 72B FP16 benchmark data measured or estimated?
- Qwen2.5 72B FP16 currently uses estimated baseline anchors on this page. Verified benchmark data will replace the estimate after a local hardware run is available.
Ollama Model Size and RAM Notes
| Ollama tag | qwen2.5:72b |
|---|---|
| Run command | ollama run qwen2.5:72b |
| Model size guidance | FP16 quantization, 72 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 50GB minimum, 62GB optimal |
| System RAM planning | 128GB 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 | 50GB minimum / 62GB optimal |
|---|---|
| System RAM requirement | 128GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Not recommended |
| 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 |
|---|---|---|---|
| Qwen2.5 72B FP16 | FP16 | 50GB min / 62GB optimal | qwen2.5:72b |
| Qwen2.5 72B Q4 | Q4 | 38GB min / 48GB optimal | qwen2.5:72b |
| Qwen2.5 72B Q5 | Q5 | 40GB min / 50GB optimal | qwen2.5:72b |
| Qwen2.5 72B Q8 | Q8 | 44GB min / 54GB optimal | qwen2.5:72b |
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)
Real benchmark data not available yet for this tag. Estimated anchors are shown above.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for Qwen2.5 72B FP16
- Local run: RTX 3090 (24GB) (Check latest deal) for around 3.7 tok/s on this profile.
- Cloud run: RunPod A100 80GB , about 2.4x 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 | $102 | $306 |
Related Model Profiles
- Qwen2.5 72B Q4 38GB min, 48GB optimal
- Qwen2.5 72B Q5 40GB min, 50GB optimal
- Qwen2.5 72B Q8 44GB min, 54GB optimal
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This page currently uses estimated benchmark baselines. Measured data will replace it after validation.