Llama 4 Maverick 17B 128E needs ~273.4 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~38 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
17.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~15.6 GB host RAM)
Decode
37.9 tok/s
TTFT
5109 ms
Safe context
4K
Memory
273.4 GB / 256.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 15.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs with offload (needs ~14.3 GB host RAM) | 38.3 tok/s | 2755 ms | 4K |
| Coding | A | Runs with offload (needs ~15.6 GB host RAM) | 37.9 tok/s | 5109 ms | 4K |
| Agentic Coding | A | Runs with offload (needs ~18 GB host RAM) | 37.1 tok/s | 7599 ms | 4K |
| Reasoning | A | Runs with offload (needs ~15.6 GB host RAM) | 37.9 tok/s | 6037 ms | 4K |
| RAG | A | Runs with offload (needs ~18 GB host RAM) | 37.1 tok/s | 9499 ms | 4K |
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 156.0 GB | Low | A82 |
Q3_K_SBest for your GPU | 3 | 196.0 GB | Low | A82 |
NVFP4 | 4 | 224.0 GB | Medium | F0 |
Q4_K_M | 4 | 244.0 GB | Medium | F0 |
Q5_K_M | 5 | 288.0 GB | High | F0 |
Q6_K | 6 | 328.0 GB | High | F0 |
Q8_0 | 8 | 428.0 GB | Very High | F0 |
F16 | 16 | 820.0 GB | Maximum | F0 |
Copy-paste commands to run Llama 4 Maverick 17B 128E on your machine.
Run
lms load Llama-4-Maverick-17B-128E-Instruct && lms server startYes, AMD Instinct MI325X 256GB can run Llama 4 Maverick 17B 128E with a A grade (Runs with offload (needs ~15.6 GB host RAM)). Expected decode speed: 37.9 tok/s.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 273.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 4 Maverick 17B 128E is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI325X 256GB, Llama 4 Maverick 17B 128E achieves approximately 37.9 tokens per second decode speed with a time-to-first-token of 5109ms using Q4_K_M quantization.
For coding workloads, Llama 4 Maverick 17B 128E on AMD Instinct MI325X 256GB receives a A grade with 37.9 tok/s and 4K context.
On AMD Instinct MI325X 256GB, Llama 4 Maverick 17B 128E can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-4-maverick-17b-128e-on-instinct-mi325x-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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