Solar Open 100B needs ~76.3 GB but RTX 3090 24GB only has 24.0 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
52.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
76.3 GB / 24.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 76.3 GB, but this setup only exposes 24.0 GB of usable VRAM.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Solar Open 100B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 9.8 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 9.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 7.6 | Tight |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 7.2 | Tight |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 5.1 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 4.9 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 3.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.8 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.7 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.5 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.2 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 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.
How Solar Open 100B (100B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | F0 |
Q3_K_S | 3 | 49.0 GB | Low | F0 |
NVFP4 | 4 | 56.0 GB | Medium | F0 |
Q4_K_M | 4 | 61.0 GB | Medium | F0 |
Q5_K_M | 5 | 72.0 GB | High | F0 |
Q6_K | 6 | 82.0 GB | High | F0 |
Q8_0 | 8 | 107.0 GB | Very High | F0 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Upgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickMakes 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.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueMakes 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.
~$9,999 MSRP
Makes 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.
~$12,000 MSRP
NVIDIA DGX Spark 128GBNVIDIA 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.
No, Solar Open 100B requires more memory than RTX 3090 24GB provides.
Solar Open 100B (100B parameters) requires approximately 76.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3090 24GB, Solar Open 100B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, Solar Open 100B on RTX 3090 24GB receives a F grade with 2.0 tok/s and 4K context.
On RTX 3090 24GB, Solar Open 100B 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.
Paste this snippet into any page to show a live fit card.
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