RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+784)
B
Raises estimated decode speed by about 68%.82 tok/sデコード
Raises estimated decode speed by about 68%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 run on RTX 3090 Ti 24GB?
YES — Tight Fit
C52Usable
Estimated from fit model
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~21.1 GB VRAM. RTX 3090 Ti 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 tok/s.
Runtime: OllamaCapacity: TightBandwidth: 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) — 21.1 GB, 48.9 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
48.9 tok/s
TTFT
3960 ms
Safe context
33K
Memory
21.1 GB / 24.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsDolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on RTX 3090 Ti 24GB
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: 48.9 tok/s decode · 4.0s TTFT (warm) · 122 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 | 48.9 tok/s | 2160 ms | 33K |
| Coding | C | Tight fit | 48.9 tok/s | 3960 ms | 33K |
| Agentic Coding | C | Runs with offload | 48.9 tok/s | 5760 ms | 33K |
| Reasoning | C | Tight fit | 48.9 tok/s | 4680 ms | 33K |
| RAG | C | Runs with offload | 48.9 tok/s | 7201 ms | 33K |
Quantization options
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on RTX 3090 Ti 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C48 |
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 |
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アップグレードオプション
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1を快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.51.4 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can RTX 3090 Ti 24GB run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1?
Yes, RTX 3090 Ti 24GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 with a C grade (Tight fit). Expected decode speed: 48.9 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 21.1 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 RTX 3090 Ti 24GB?
On RTX 3090 Ti 24GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 48.9 tokens per second decode speed with a time-to-first-token of 3960ms using Q4_K_M quantization.
Can RTX 3090 Ti 24GB 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 RTX 3090 Ti 24GB receives a C grade with 48.9 tok/s and 33K context.
What context window can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 use on RTX 3090 Ti 24GB?
On RTX 3090 Ti 24GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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