NVIDIA A100 40GB经济之选40 GB VRAM (+8)1555 GB/s (+655)
C
Raises estimated decode speed by about 117%.89.2 tok/s 解码
Raises estimated decode speed by about 117%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 run on NVIDIA V100 32GB?
YES — Runs Great
C54Usable
Estimated from fit model
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~21.9 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 21.9 GB, 41.2 tok/s, Runs well
21.9 GB required32.0 GB available
68% VRAM used
Fit status
Runs well
Decode
41.2 tok/s
TTFT
4700 ms
Safe context
74K
Memory
21.9 GB / 32.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsDolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on NVIDIA V100 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 41.2 tok/s | 2564 ms | 74K |
| Coding | C | Runs well | 41.2 tok/s | 4700 ms | 74K |
| Agentic Coding | C | Runs well | 41.2 tok/s | 6837 ms | 74K |
| Reasoning | C | Runs well | 41.2 tok/s | 5555 ms | 74K |
| RAG | C | Runs well | 41.2 tok/s | 8546 ms | 74K |
Quantization options
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C46 |
Q3_K_S | 3 | 11.8 GB | Low | C47 |
NVFP4 | 4 | 13.4 GB | Medium | C48 |
Q4_K_M | 4 | 14.6 GB | Medium | C48 |
Q5_K_M | 5 | 17.3 GB | High | C49 |
Q6_K | 6 | 19.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C48 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Get started
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升级选项
能流畅运行 Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 的硬件
Frequently asked questions
Can NVIDIA V100 32GB run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1?
Yes, NVIDIA V100 32GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 with a C grade (Runs well). Expected decode speed: 41.2 tok/s.
How much VRAM does Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 need?
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B parameters) requires approximately 21.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1?
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.
What speed will Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 run at on NVIDIA V100 32GB?
On NVIDIA V100 32GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 41.2 tokens per second decode speed with a time-to-first-token of 4700ms using Q4_K_M quantization.
Can NVIDIA V100 32GB run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 for coding?
For coding workloads, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on NVIDIA V100 32GB receives a C grade with 41.2 tok/s and 74K context.
What context window can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 use on NVIDIA V100 32GB?
On NVIDIA V100 32GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 74K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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