RX 7900 XTX 24GBBudget pick
960 GB/s (+560)
C
Raises estimated decode speed by about 136%.112 tok/s decode
Raises estimated decode speed by about 136%.
~$999 MSRP
dolphin 2.9.4 llama3.1 8b needs ~10.2 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~48 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
47.5 tok/s
TTFT
4072 ms
Safe context
236K
Memory
10.2 GB / 23.0 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 | 47.5 tok/s | 2221 ms | 236K |
| Coding | C | Runs well | 47.5 tok/s | 4072 ms | 236K |
| Agentic Coding | C | Runs well | 47.5 tok/s | 5923 ms | 236K |
| Reasoning | C | Runs well | 47.5 tok/s | 4813 ms | 236K |
| RAG | C | Runs well | 47.5 tok/s | 7404 ms | 236K |
How dolphin 2.9.4 llama3.1 8b (8B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run dolphin 2.9.4 llama3.1 8b on your machine.
Run
lms load hf-bartowski--dolphin-2-9-4-llama3-1-8b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 136%.
~$999 MSRP
RTX 3090 24GBBest valueRaises estimated decode speed by about 102%.
~$1,499 MSRP
Yes, MacBook Pro M2 Max 32GB can run dolphin 2.9.4 llama3.1 8b with a C grade (Runs well). Expected decode speed: 47.5 tok/s.
dolphin 2.9.4 llama3.1 8b (8B parameters) requires approximately 10.2 GB of memory with Q4_K_M quantization.
The recommended quantization for dolphin 2.9.4 llama3.1 8b is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Max 32GB, dolphin 2.9.4 llama3.1 8b achieves approximately 47.5 tokens per second decode speed with a time-to-first-token of 4072ms using Q4_K_M quantization.
For coding workloads, dolphin 2.9.4 llama3.1 8b on MacBook Pro M2 Max 32GB receives a C grade with 47.5 tok/s and 236K context.
On MacBook Pro M2 Max 32GB, dolphin 2.9.4 llama3.1 8b can safely use up to 236K 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-bartowski--dolphin-2-9-4-llama3-1-8b-gguf-on-m2-max-32gb" 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 |
| C45 |
Q4_K_M | 4 | 4.9 GB | Medium | C46 |
Q5_K_M | 5 | 5.8 GB | High | C46 |
Q6_K | 6 | 6.6 GB | High | C47 |
Q8_0 | 8 | 8.6 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C50 |
Not always. MacBook Pro M2 Max 32GB 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.