Llama 4 Maverick 17B 128E needs ~187.5 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q2_K quantization, expect ~15 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
91.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.7 tok/s
TTFT
28822 ms
Safe context
4K
Memory
275.5 GB / 184.3 GB
Offload
30%
This setup is broadly balanced for this model.
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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 6.8 tok/s | 15626 ms | 4K |
| Coding | F | Too heavy | 6.7 tok/s | 28822 ms | 4K |
| Agentic Coding | F | Too heavy | 6.6 tok/s | 42430 ms | 4K |
| Reasoning | F | Too heavy | 6.7 tok/s | 34062 ms | 4K |
| RAG | F | Too heavy | 6.6 tok/s | 53037 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 4 Maverick 17B 128E at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~7 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 6.7 | Too big |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 4.3 | Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 4.1 | Too big |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 3.2 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 3.2 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 2.4 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 2.3 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.2 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 4 Maverick 17B 128E (400B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 156.0 GB | Low | F0 |
Q3_K_S | 3 | 196.0 GB | Low | F0 |
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 startOpções de upgrade
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 466%.
~$20,000 MSRP
Yes, Mac Studio M3 Ultra 256GB can run Llama 4 Maverick 17B 128E at Q2_K quantization (Runs with offload (needs ~2.6 GB host RAM)). The recommended Q4_K_M requires 275.5 GB which exceeds available memory, but at Q2_K it needs only 187.5 GB. Expected decode speed: 14.6 tok/s.
Llama 4 Maverick 17B 128E (400B parameters) requires approximately 275.5 GB at Q4_K_M quantization. On Mac Studio M3 Ultra 256GB, it fits at Q2_K using 187.5 GB.
The recommended quantization is Q4_K_M, but on Mac Studio M3 Ultra 256GB the best fitting quantization is Q2_K, which uses 187.5 GB.
On Mac Studio M3 Ultra 256GB, Llama 4 Maverick 17B 128E achieves approximately 14.6 tokens per second decode speed with a time-to-first-token of 13234ms using Q2_K quantization.
For coding workloads, Llama 4 Maverick 17B 128E on Mac Studio M3 Ultra 256GB receives a F grade with 6.7 tok/s and 4K context.
On Mac Studio M3 Ultra 256GB, Llama 4 Maverick 17B 128E can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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