LLM-jp-4.1 32B-A3B Thinking VRAM requirements
LLM-jp-4.1 32B-A3B Thinking has 32.1B parameters, of which about 3.8B are used per token; all 128 experts still have to be in memory. With an 8K-token context and one request it needs about 21.0 GB of GPU memory at Q4_K_M, 34.0 GB at FP8 and 66.9 GB at FP16/BF16. The published weights take 59.9 GB (BF16). The smallest setup here that holds it at Q4_K_M with an 8K context is the RTX 3090 (24 GB), which runs it with up to 44K tokens of context.
21.0 GB at Q4_K_M, 8K context, one request
- FP8
- 34.0 GB
- FP16 / BF16
- 66.9 GB
- Published weights
- 59.9 GB
- Smallest setup, Q4_K_M
- RTX 3090 (24 GB)
Worked example: LLM-jp-4.1 32B-A3B Thinking with 32K tokens
Inputs: LLM-jp-4.1 32B-A3B Thinking · Q4_K_M weights · 32,768 tokens of context · one request · FP16 KV cache
- Weights (32.1B at Q4_K_M)
- 18.1 GB
- KV cache (64 KB per token)
- 2.00 GB
- Buffers and runtime (0.5 GB + 10%)
- 2.51 GB
- Total
- 22.6 GB
The smallest setup here that holds it is the RTX 3090 (24 GB), with about 1.38 GB to spare. Change the inputs in the calculator
What makes LLM-jp-4.1 32B-A3B Thinking's memory use different
As a mixture-of-experts model it reads about 2.16 GB of its 18.1 GB Q4_K_M weights per generated token (12%), so it writes like a much smaller model while needing memory for all of them.
How much VRAM does LLM-jp-4.1 32B-A3B Thinking need?
LLM-jp-4.1 32B-A3B Thinking needs 21.0 GB at Q4_K_M, 36.0 GB at Q8_0 and 66.9 GB at FP16 with 8,192 tokens of context and one request. Each total below is the weights plus the FP16 KV cache and the runtime overhead (0.5 GB plus 10%), and each row opens the calculator with that setting. What the GGUF names mean.
| Precision | Weights | Total | Smallest setup |
|---|---|---|---|
| As published (BF16) | 59.9 GB | 66.9 GB | A100 80GB |
| FP16 / BF16 | 59.9 GB | 66.9 GB | A100 80GB |
| FP8 / INT8 | 29.9 GB | 34.0 GB | M4 Pro Mac (64 GB, 48 GB usable) |
| INT4 (AWQ / GPTQ) | 15.9 GB | 18.5 GB | RTX 3090 (24 GB) |
| GGUF Q8_0 | 31.8 GB | 36.0 GB | M4 Pro Mac (64 GB, 48 GB usable) |
| GGUF Q6_K | 24.5 GB | 28.0 GB | RTX 5090 (32 GB) |
| GGUF Q5_K_M | 21.2 GB | 24.4 GB | RTX 5090 (32 GB) |
| GGUF Q4_K_M | 18.1 GB | 21.0 GB | RTX 3090 (24 GB) |
| GGUF IQ4_XS | 16.3 GB | 19.0 GB | RTX 3090 (24 GB) |
| GGUF Q3_K_M | 14.6 GB | 17.1 GB | RTX 3090 (24 GB) |
| GGUF IQ3_XXS | 12.3 GB | 14.6 GB | RTX 4060 Ti 16GB |
| GGUF Q2_K | 12.5 GB | 14.8 GB | RTX 4060 Ti 16GB |
Fine-tuning LLM-jp-4.1 32B-A3B Thinking? LLM-jp-4.1 32B-A3B Thinking VRAM for LoRA, QLoRA and full training.
LLM-jp-4.1 32B-A3B Thinking at 8K, 32K and 64K (full) tokens of context
All 32 layers use full attention. Each extra token of context adds 64 KB of FP16 cache per request. The last column is the smallest setup here, one card or a group, that holds it at Q4_K_M. How the KV cache works.
| Context | KV cache, FP16 | Total at Q4_K_M | Total at Q8_0 | Smallest setup, Q4_K_M |
|---|---|---|---|---|
| 8K tokens | 512 MB | 21.0 GB | 36.0 GB | RTX 3090 (24 GB) |
| 32K tokens | 2.00 GB | 22.6 GB | 37.7 GB | RTX 3090 (24 GB) |
| 64K tokens | 4.00 GB | 24.8 GB | 39.9 GB | RTX 5090 (32 GB) |
How fast LLM-jp-4.1 32B-A3B Thinking writes
Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth and the parameters read per token. A dash means it does not fit on one card. Try other settings in the speed calculator.
| Hardware | Bandwidth | Q4_K_M | FP8 |
|---|---|---|---|
| RTX 3060 12GB | 360 GB/s | — | — |
| RTX 4090 | 1,008 GB/s | 91–162 | — |
| RTX 5090 | 1,792 GB/s | 147–271 | — |
| M4 Max Mac (128 GB) | 546 GB/s | 53–91 | 36–61 |
| M3 Ultra Mac Studio (512 GB) | 819 GB/s | 76–134 | 52–90 |
| H100 SXM | 3,350 GB/s | 231–454 | 171–322 |
| H200 | 4,800 GB/s | 287–592 | 221–431 |
Longest context on one GPU
How many tokens of context LLM-jp-4.1 32B-A3B Thinking fits on a single card with one request, an FP16 KV cache and 0.5 GB left free. "Full" means the model's whole context window fits.
| GPU | Memory | Q4_K_M | Q8_0 | FP8 |
|---|---|---|---|---|
| RTX 3060 | 12 GB | No | No | No |
| RTX 4060 Ti 16GB | 16 GB | No | No | No |
| RTX 3090 / 4090 | 24 GB | 44K | No | No |
| RTX 5090 | 32 GB | 64K (full) | No | No |
| A100 40GB | 40 GB | 64K (full) | 58K | 64K (full) |
| Mac, 64 GB unified memory | 48 GB | 64K (full) | 64K (full) | 64K (full) |
| L40S / RTX 6000 Ada | 48 GB | 64K (full) | 64K (full) | 64K (full) |
| A100 / H100 80GB | 80 GB | 64K (full) | 64K (full) | 64K (full) |
| Mac, 128 GB unified memory | 96 GB | 64K (full) | 64K (full) | 64K (full) |
| H200 | 141 GB | 64K (full) | 64K (full) | 64K (full) |
| B200 | 180 GB | 64K (full) | 64K (full) | 64K (full) |
Model details
- Parameters
- 32.1B (32,139,028,992)
- Experts
- 128 routed experts, all loaded
- Active per token
- 3.8B
- Layers
- All 32 layers use full attention
- Attention cache
- 4 KV heads × 128
- Context length
- 65,536 tokens
- Published weights
- 59.9 GB (BF16)
- On Hugging Face
- llm-jp/
llm-jp-4.1-32b-a3b-thinking
Why this estimate looks this way
At Q4_K_M and an 8K-token context, LLM-jp-4.1 32B-A3B Thinking uses 18.1 GB for weights, 512 MB for its FP16 KV cache and 2.36 GB for estimated runtime overhead, totaling 21.0 GB. The overhead is a 0.5 GB base plus 10% of weights and cache.
This is a mixture-of-experts model: all 128 experts contribute to the 32.1B parameters held in memory, even though only about 3.8B parameters run per token. Using only active parameters would understate VRAM.
The architecture and published checkpoint size come from the model's config.json and weight files. These are estimates rather than measured peak memory; inference engines can reserve extra buffers or preallocate the full configured cache.
LLM-jp-4.1 32B-A3B Thinking next to similar-sized models
The three models here closest to it in parameter count, at Q4_K_M with 8,192 tokens of context.
| Model | Parameters | Total | KV per token | Smallest setup |
|---|---|---|---|---|
| LLM-jp-4.1 32B-A3B Thinking | 32.1B, 3.8B active | 21.0 GB | 64 KB | RTX 3090 (24 GB) |
| Nemotron 3 Nano 30B-A3B | 31.6B, 3.5B active | 20.1 GB | 6 KB | RTX 3090 (24 GB) |
| Gemma 4 31B | 31.3B | 21.9 GB | 80 KB | RTX 3090 (24 GB) |
| GLM-4.7 Flash | 31.2B, 3B active | 20.3 GB | 53 KB | RTX 3090 (24 GB) |
Badge for your model card
Paste it into a Hugging Face or GitHub README; it shows the Q4_K_M total above and links to this page. Q8_0 and FP16 badges.
Numbers read from the model files on Hugging Face on . Compare it with other models in the reproducible model and GPU report.