Can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 run on NVIDIA H100 80GB?

YES — Runs Great

C49Usable

Estimated from fit model

Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~26.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~192 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.7 GB, 192.2 tok/s, Runs well

26.7 GB required80.0 GB available

33% VRAM used

Fit status

Runs well

Decode

192.2 tok/s

TTFT

1007 ms

Safe context

319K

Memory

26.7 GB / 80.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsDolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on NVIDIA H100 80GB
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: 192.2 tok/s decode · 1.0s TTFT (warm) · 481 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	192.2 tok/s	549 ms	319K
Coding	C	Runs well	192.2 tok/s	1007 ms	319K
Agentic Coding	C	Runs well	192.2 tok/s	1465 ms	319K
Reasoning	C	Runs well	192.2 tok/s	1190 ms	319K
RAG	C	Runs well	192.2 tok/s	1831 ms	319K

Inference speed

Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	82.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	52.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	47.2	Tight
RTX 3090 24GB	24 GB	Q4_K_M	44.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	38.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	31.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	30.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	19.1	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	16.4	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	15.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	6.7	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C40
Q3_K_S	3	11.8 GB	Low	C40
NVFP4	4	13.4 GB	Medium	C41
Q4_K_M	4	14.6 GB	Medium	C41
Q5_K_M	5	17.3 GB	High	C41
Q6_K	6	19.7 GB	High	C42
Q8_0	8	25.7 GB	Very High	C43
F16Best for your GPU	16	49.2 GB	Maximum	C48

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

Frequently asked questions

Can NVIDIA H100 80GB run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1?

Yes, NVIDIA H100 80GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 with a C grade (Runs well). Expected decode speed: 192.2 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 26.7 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 H100 80GB?

On NVIDIA H100 80GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 192.2 tokens per second decode speed with a time-to-first-token of 1007ms using Q4_K_M quantization.

Can NVIDIA H100 80GB 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 H100 80GB receives a C grade with 192.2 tok/s and 319K context.

What context window can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 319K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1

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