Llama 4 16X17B Q4 VRAM Requirement and Ollama Model Size
Llama 4 16X17B Q4 needs 40GB minimum VRAM, 48GB optimal VRAM, and 64GB+ system RAM for a safer Ollama run. Top-20 curated profile from ollama.com popular list.. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | Llama 4 |
|---|---|
| Scenario | multimodal |
| License scope | closed-weight |
| Quantization | Q4 |
| VRAM minimum | 40GB |
| VRAM optimal | 48GB |
| 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 | llama4:16x17b |
| Popularity | New Frontier Model |
| Category | Multimodal / MoE |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 1.1 |
| RTX 4090 24GB | 1.5 |
| A100 80GB | 2.6 |
Quick Answers
- How much VRAM does Llama 4 16X17B Q4 need?
- Llama 4 16X17B Q4 needs about 40GB minimum VRAM and 48GB optimal VRAM for a safer local run target.
- Can Llama 4 16X17B Q4 run on an RTX 3090 24GB?
- Not as a comfortable local target. Llama 4 16X17B Q4 needs about 40GB minimum and 48GB optimal VRAM, so cloud or larger local GPUs are safer.
- What is the Ollama command for Llama 4 16X17B Q4?
- Use ollama run llama4:16x17b. Check the Ollama tag llama4:16x17b before running.
- What is the approximate Ollama model size for Llama 4 16X17B Q4?
- Llama 4 16X17B Q4 uses the llama4:16x17b Ollama tag. Treat the Q4 272 profile as smaller than the runtime VRAM budget; use 40GB minimum VRAM and 48GB optimal VRAM as the safer sizing numbers.
- How much system RAM does Llama 4 16X17B Q4 need in Ollama?
- Plan for at least 64GB system RAM alongside 40GB minimum VRAM and 48GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for Llama 4 16X17B Q4?
- Llama 4 16X17B Q4 should be sized by GPU VRAM first: 40GB minimum and 48GB optimal. System RAM should be at least 64GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is Llama 4 16X17B Q4 VRAM usage on an RTX 3090?
- Llama 4 16X17B Q4 is not recommended for an RTX 3090 24GB target. The page estimates 40GB minimum and 48GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the llama4:16x17b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: Llama 4 16X17B Q4 (Q4, 40GB min/48GB optimal); Llama 4 16X17B Q5 (Q5, 48GB min/50GB optimal); Llama 4 16X17B Q8 (Q8, 219GB min/229GB optimal); Llama 4 16X17B FP16 (FP16, 225GB min/237GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is Llama 4 16X17B Q4 benchmark data measured or estimated?
- Llama 4 16X17B Q4 has a verified RTX 3090 result of 8.58 tokens/s from LocalVRAM benchmark data.
Ollama Model Size and RAM Notes
| Ollama tag | llama4:16x17b |
|---|---|
| Run command | ollama run llama4:16x17b |
| Model size guidance | Q4 quantization, 272 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 40GB minimum, 48GB 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 | 40GB minimum / 48GB optimal |
|---|---|
| System RAM requirement | 64GB 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 |
|---|---|---|---|
| Llama 4 16X17B Q4 | Q4 | 40GB min / 48GB optimal | llama4:16x17b |
| Llama 4 16X17B Q5 | Q5 | 48GB min / 50GB optimal | llama4:16x17b |
| Llama 4 16X17B Q8 | Q8 | 219GB min / 229GB optimal | llama4:16x17b |
| Llama 4 16X17B FP16 | FP16 | 225GB min / 237GB optimal | llama4:16x17b |
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 | 8.58 |
|---|---|
| Latency | 8259 ms |
| Prompt tokens | 363 |
| Eval tokens | 64 |
| 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 Llama 4 16X17B Q4
- Local run: RTX 3090 (24GB) (Check latest deal) for around 8.58 tok/s on this profile.
- Cloud run: RunPod A100 80GB , about 0.3x 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
- Llama 4 16X17B Q5 48GB min, 50GB optimal
- Llama 4 16X17B Q8 219GB min, 229GB optimal
- Llama 4 16X17B FP16 225GB min, 237GB optimal
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