Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Llama 3 8B Instruct 32k v0.1 needs ~19.5 GB VRAM. Gaudi 3 128GB has 128.0 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
1.9M
Memory
19.5 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 112.0 tok/s | 943 ms | 1.9M |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 1.9M |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 1.9M |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 1.9M |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 1.9M |
How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D38 |
Q3_K_S | 3 | 3.9 GB | Low | D38 |
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 startUpgrade options
Yes, Gaudi 3 128GB 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 19.5 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 Gaudi 3 128GB, 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 Gaudi 3 128GB receives a C grade with 112.0 tok/s and 1.9M context.
On Gaudi 3 128GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 1.9M 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-gaudi-3-128gb" 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 |
| D38 |
Q4_K_M | 4 | 4.9 GB | Medium | D38 |
Q5_K_M | 5 | 5.8 GB | High | D38 |
Q6_K | 6 | 6.6 GB | High | D38 |
Q8_0 | 8 | 8.6 GB | Very High | D38 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | D39 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.