cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~19.2 GB but Intel Arc A580 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
11.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.6 tok/s
TTFT
75316 ms
Safe context
4K
Memory
19.2 GB / 8.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 19.2 GB, but this setup only exposes 8.0 GB of usable VRAM.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.6 tok/s | 41082 ms | 4K |
| Coding | F | Too heavy | 2.6 tok/s | 75316 ms | 4K |
| Agentic Coding | F | Too heavy | 2.6 tok/s | 109551 ms | 4K |
| Reasoning | F | Too heavy | 2.6 tok/s | 89010 ms | 4K |
| RAG | F | Too heavy | 2.6 tok/s | 136939 ms | 4K |
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | F0 |
Q3_K_S | 3 | 11.8 GB | Low | F0 |
NVFP4 | 4 | 13.4 GB | Medium | F0 |
Q4_K_M | 4 | 14.6 GB | Medium | F0 |
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 |
Upgrade-Optionen
Intel Arc Pro B60 24GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $599 MSRP
RTX 5090 32GBGrößter SprungMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $1,999 MSRP
Intel Data Center GPU Max 1550 128GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $15,000 MSRP
Gaudi 3 128GBIntel-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $15,000 MSRP
No, cognitivecomputations Dolphin3.0 R1 Mistral 24B requires more memory than Intel Arc A580 8GB provides.
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 19.2 GB of memory with Q4_K_M quantization.
The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A580 8GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 2.6 tokens per second decode speed with a time-to-first-token of 75316ms using Q4_K_M quantization.
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on Intel Arc A580 8GB receives a F grade with 2.6 tok/s and 4K context.
On Intel Arc A580 8GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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