Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~21.8 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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
Tight fit
Decode
8.9 tok/s
TTFT
21870 ms
Safe context
23K
Memory
21.8 GB / 23.0 GB
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 | C | Tight fit | 8.9 tok/s | 11929 ms | 23K |
| Coding | C | Tight fit | 8.9 tok/s | 21870 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~0.9 GB host RAM) | 7.9 tok/s | 35790 ms | 23K |
| Reasoning | C | Tight fit | 8.9 tok/s | 25846 ms | 23K |
| RAG | D | Runs with offload (needs ~0.9 GB host RAM) | 7.9 tok/s | 44738 ms | 23K |
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C49 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_M | 4 | 14.6 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | C49 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Copy-paste commands to run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on your machine.
Run
lms load hf-mradermacher--dolphin-mistral-glm-4-7-flash-24b-venice-edition-thinking-uncensored-i1-gguf && lms server startアップグレードオプション
Raises estimated decode speed by about 142%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Raises estimated decode speed by about 284%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Yes, MacBook Pro M4 32GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 with a C grade (Tight fit). Expected decode speed: 8.9 tok/s.
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B parameters) requires approximately 21.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 32GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21870ms using Q4_K_M quantization.
For coding workloads, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on MacBook Pro M4 32GB receives a C grade with 8.9 tok/s and 23K context.
On MacBook Pro M4 32GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 23K tokens of context. The model's official context limit is —, 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. MacBook Pro M4 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--dolphin-mistral-glm-4-7-flash-24b-venice-edition-thinking-uncensored-i1-gguf-on-m4-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: