Llama 3 8B Instruct 32k v0.1 needs ~34.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~112 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
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
2.6M
Memory
34.4 GB / 184.3 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 112.0 tok/s | 943 ms | 2.6M |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 2.6M |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 2.6M |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 2.6M |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 2.6M |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D37 |
Q3_K_S | 3 | 3.9 GB | Low | D37 |
NVFP4 | 4 |
Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.
Run
lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server startYes, Mac Studio M3 Ultra 256GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 34.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.
On Mac Studio M3 Ultra 256GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
For coding workloads, Llama 3 8B Instruct 32k v0.1 on Mac Studio M3 Ultra 256GB receives a C grade with 112.0 tok/s and 2.6M context.
On Mac Studio M3 Ultra 256GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 2.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf-on-m3-ultra-256gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| D37 |
Q4_K_M | 4 | 4.9 GB | Medium | D37 |
Q5_K_M | 5 | 5.8 GB | High | D37 |
Q6_K | 6 | 6.6 GB | High | D37 |
Q8_0 | 8 | 8.6 GB | Very High | D37 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | D37 |
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.