RX 7900 XTX 24GBOpción económica
24 GB VRAM (+4)960 GB/s (+160)
C
Sube la velocidad estimada de decodificación alrededor de un 99%.47.2 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 99%.
~$999 MSRP
Can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 run on RX 7900 XT 20GB?
YES — With Offload
C49Usable
Estimated from fit model
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~20.4 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~24 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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) — 20.4 GB, 23.7 tok/s, Runs with offload (needs ~0.3 GB host RAM)
20.4 GB required20.0 GB available
102% VRAM needed
0.4 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
23.7 tok/s
TTFT
8168 ms
Safe context
14K
Memory
20.4 GB / 20.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsDolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on RX 7900 XT 20GB
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: 23.7 tok/s decode · 8.2s TTFT (warm) · 59 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 32.8 tok/s | 3221 ms | 14K |
| Coding | C | Runs with offload (needs ~0.3 GB host RAM) | 23.7 tok/s | 8168 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~2 GB host RAM) | 18.0 tok/s | 15602 ms | 14K |
| Reasoning | C | Runs with offload (needs ~0.3 GB host RAM) | 23.7 tok/s | 9653 ms | 14K |
| RAG | D | Very compromised (needs ~2 GB host RAM) | 18.0 tok/s | 19502 ms | 14K |
Quantization options
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C51 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 14.6 GB | Medium | C50 |
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 |
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 startOpciones de mejora
Hardware que ejecuta bien Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1
Radeon AI PRO R9700 32GBMejor relación calidad-precio
32 GB VRAM (+12)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.25.8 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$1,899 MSRP
Radeon Pro W6800 32GBMejora AMD
32 GB VRAM (+12)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.19.6 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$2,249 MSRP
Frequently asked questions
Can RX 7900 XT 20GB run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1?
Yes, RX 7900 XT 20GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 with a C grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 23.7 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 20.4 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 RX 7900 XT 20GB?
On RX 7900 XT 20GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 23.7 tokens per second decode speed with a time-to-first-token of 8168ms using Q4_K_M quantization.
Can RX 7900 XT 20GB 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 RX 7900 XT 20GB receives a C grade with 23.7 tok/s and 14K context.
What context window can Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 use on RX 7900 XT 20GB?
On RX 7900 XT 20GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 feels slow on RX 7900 XT 20GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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