Command R+ 104B needs ~80.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~3 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
2.8 tok/s
TTFT
68949 ms
Safe context
131K
Memory
80.8 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 | B | Runs well | 2.8 tok/s | 37608 ms | 131K |
| Coding | B | Runs well | 2.8 tok/s | 68949 ms | 131K |
| Agentic Coding | B | Runs well | 2.8 tok/s | 100289 ms | 131K |
| Reasoning | B | Runs well | 2.8 tok/s | 81485 ms | 131K |
| RAG | B | Runs well | 2.8 tok/s | 125361 ms | 131K |
How Command R+ 104B (104B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 40.6 GB | Low | B63 |
Q3_K_S | 3 | 51.0 GB | Low | B65 |
NVFP4 | 4 | 58.2 GB | Medium | B65 |
Q4_K_M | 4 | 63.4 GB | Medium | B65 |
Q5_K_MBest for your GPU | 5 | 74.9 GB | High | B65 |
Q6_K | 6 | 85.3 GB | High | F0 |
Q8_0 | 8 | 111.3 GB | Very High | F0 |
F16 | 16 | 213.2 GB | Maximum | F0 |
Copy-paste commands to run Command R+ 104B on your machine.
Run
ollama run command-r-plus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 2368%.
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 2368%.
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 4014%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Command R+ 104B with a B grade (Runs well). Expected decode speed: 2.8 tok/s.
Command R+ 104B (104B parameters) requires approximately 80.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Command R+ 104B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA DGX Spark 128GB, Command R+ 104B achieves approximately 2.8 tokens per second decode speed with a time-to-first-token of 68949ms using Q4_K_M quantization.
For coding workloads, Command R+ 104B on NVIDIA DGX Spark 128GB receives a B grade with 2.8 tok/s and 131K context.
On NVIDIA DGX Spark 128GB, Command R+ 104B can safely use up to 131K tokens of context. The model's official context limit is 131K, 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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