Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 needs ~66.3 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~5 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
11.2 tok/s
TTFT
17303 ms
Safe context
455K
Memory
31.7 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52435 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96130 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 139826 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 113608 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 174782 ms | 4K |
How Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | D39 |
Q3_K_S | 3 | 11.8 GB | Low | D40 |
NVFP4 | 4 | 13.4 GB | Medium | D40 |
Q4_K_M | 4 | 14.6 GB | Medium | C40 |
Q5_K_M | 5 | 17.3 GB | High | C40 |
Q6_K | 6 | 19.7 GB | High | C41 |
Q8_0 | 8 | 25.7 GB | Very High | C42 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C47 |
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-Optionen
NVIDIA H200 141GBGünstige WahlRaises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 2359%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
NVIDIA B200 180GBNVIDIA-UpgradeRaises estimated decode speed by about 2900%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 at F16 quantization (Runs well). The recommended Q4_K_M requires 18.7 GB which exceeds available memory, but at F16 it needs only 66.3 GB. Expected decode speed: 4.7 tok/s.
Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 (24B parameters) requires approximately 18.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 66.3 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 66.3 GB.
On NVIDIA DGX Spark 128GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 achieves approximately 4.7 tokens per second decode speed with a time-to-first-token of 41536ms using F16 quantization.
For coding workloads, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Dolphin Mistral GLM 4.7 Flash 24B Venice Edition Thinking Uncensored i1 can safely use up to 258K tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. NVIDIA DGX Spark 128GB 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.
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