Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~19.7 GB VRAM. Mac mini M2 24GB has 17.3 GB. With NVFP4 quantization, expect ~4 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
3.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.3 tok/s
TTFT
57981 ms
Safe context
4K
Memory
20.9 GB / 17.3 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~1.7 GB host RAM) | 3.6 tok/s | 28933 ms | 4K |
| Coding | F | Too heavy | 3.3 tok/s | 57981 ms | 4K |
| Agentic Coding | F | Too heavy | 2.9 tok/s | 98115 ms | 4K |
| Reasoning | F | Too heavy | 3.3 tok/s | 68523 ms | 4K |
| RAG | F | Too heavy | 2.9 tok/s | 122643 ms | 4K |
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C51 |
Q3_K_SBest for your GPU | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
Q5_K_M | 5 | 17.3 GB | High | F0 |
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 startOpciones de mejora
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$799 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,099 MSRP
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,099 MSRP
RTX 5090 32GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,999 MSRP
Yes, Mac mini M2 24GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 at NVFP4 quantization (Very compromised (needs ~1.7 GB host RAM)). The recommended Q4_K_M requires 20.9 GB which exceeds available memory, but at NVFP4 it needs only 19.7 GB. Expected decode speed: 4.1 tok/s.
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B parameters) requires approximately 20.9 GB at Q4_K_M quantization. On Mac mini M2 24GB, it fits at NVFP4 using 19.7 GB.
The recommended quantization is Q4_K_M, but on Mac mini M2 24GB the best fitting quantization is NVFP4, which uses 19.7 GB.
On Mac mini M2 24GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 4.1 tokens per second decode speed with a time-to-first-token of 47023ms using NVFP4 quantization.
For coding workloads, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on Mac mini M2 24GB receives a F grade with 3.3 tok/s and 4K context.
On Mac mini M2 24GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. Mac mini M2 24GB 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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<iframe src="https://willitrunai.com/embed/hf-mradermacher--dolphin-mistral-glm-4-7-flash-24b-venice-edition-thinking-uncensored-i1-gguf-on-m2-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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