Solar Open 69B REAP i1 needs ~64.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~4 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
3.9 tok/s
TTFT
49747 ms
Safe context
104K
Memory
64.4 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 | C | Runs well | 3.9 tok/s | 27135 ms | 104K |
| Coding | C | Runs well | 3.9 tok/s | 49747 ms | 104K |
| Agentic Coding | C | Runs well | 3.9 tok/s | 72360 ms | 104K |
| Reasoning | C | Runs well | 3.9 tok/s | 58792 ms | 104K |
| RAG | C | Runs well | 3.9 tok/s | 90450 ms | 104K |
How Solar Open 69B REAP i1 (69B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.9 GB | Low | C42 |
Q3_K_S | 3 | 33.8 GB | Low | C44 |
NVFP4 | 4 | 38.6 GB | Medium | C45 |
Q4_K_M | 4 | 42.1 GB | Medium | C46 |
Q5_K_M | 5 | 49.7 GB | High | C47 |
Q6_K | 6 | 56.6 GB | High | C48 |
Q8_0Best for your GPU | 8 | 73.8 GB | Very High | C48 |
F16 | 16 | 141.5 GB | Maximum | F0 |
Copy-paste commands to run Solar Open 69B REAP i1 on your machine.
Run
lms load hf-mradermacher--solar-open-69b-reap-i1-gguf && lms server startアップグレードオプション
NVIDIA H200 141GBコスパ重視Raises estimated decode speed by about 2356%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA H200 PCIe 141GBコスパ最良Raises estimated decode speed by about 2356%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレードRaises estimated decode speed by about 3995%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Solar Open 69B REAP i1 with a C grade (Runs well). Expected decode speed: 3.9 tok/s.
Solar Open 69B REAP i1 (69B parameters) requires approximately 64.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar Open 69B REAP i1 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Solar Open 69B REAP i1 achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49747ms using Q4_K_M quantization.
For coding workloads, Solar Open 69B REAP i1 on NVIDIA DGX Spark 128GB receives a C grade with 3.9 tok/s and 104K context.
On NVIDIA DGX Spark 128GB, Solar Open 69B REAP i1 can safely use up to 104K tokens of context. 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.
Paste this snippet into any page to show a live fit card.
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