RTX PRO 5000 Blackwell 48GBBudget pick1344 GB/s (+544)
C
Raises estimated decode speed by about 143%.77.1 tok/s decode
Raises estimated decode speed by about 143%.
~$4,999 MSRP
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~25.3 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~32 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
31.7 tok/s
TTFT
6108 ms
Safe context
134K
Memory
25.3 GB / 46.1 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 | 31.7 tok/s | 3332 ms | 134K |
| Coding | C | Runs well | 31.7 tok/s | 6108 ms | 134K |
| Agentic Coding | C | Runs well | 31.7 tok/s | 8885 ms | 134K |
| Reasoning | C | Runs well | 31.7 tok/s | 7219 ms | 134K |
| RAG | C | Runs well | 31.7 tok/s | 11106 ms | 134K |
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C43 |
Q3_K_S | 3 | 11.8 GB | Low | C44 |
NVFP4 |
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 startUpgrade options
RTX PRO 5000 Blackwell 48GBBudget pickRaises estimated decode speed by about 143%.
~$4,999 MSRP
RTX 6000 Ada 48GBBest valueRaises estimated decode speed by about 70%.
~$6,800 MSRP
Yes, Mac Studio M2 Ultra 64GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 with a C grade (Runs well). Expected decode speed: 31.7 tok/s.
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B parameters) requires approximately 25.3 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 Mac Studio M2 Ultra 64GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6108ms using Q4_K_M quantization.
For coding workloads, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on Mac Studio M2 Ultra 64GB receives a C grade with 31.7 tok/s and 134K context.
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-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
13.4 GB |
| Medium |
| C44 |
Q4_K_M | 4 | 14.6 GB | Medium | C45 |
Q5_K_M | 5 | 17.3 GB | High | C45 |
Q6_K | 6 | 19.7 GB | High | C46 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C48 |
F16 | 16 | 49.2 GB | Maximum | F0 |
On Mac Studio M2 Ultra 64GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 134K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. Mac Studio M2 Ultra 64GB 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.