NVIDIA · 8x B300 SXM (Blackwell Ultra) · Server
Best AI Models for NVIDIA DGX B300 (2.3 TB)
Runs models up to 2.8T parameters, like Kimi K3.1 Pro.
2.3 TB HBM64 TB/s18 PFLOPS FP1615 kW2025
| # | Model | Speed | llmrun Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Kimi K3.1 ProMoonshot AIHow to run → Q4_K_M · 1712.1 GB · 150 t/s | 150 tok/s | Q4_K_M · 1712.1 GB | |
| 2 | Inkling 2 SmallThinking Machines Q4_K_M · 359.1 GB · 276 t/s | 276 tok/s | Q4_K_M · 359.1 GB | |
| 3 | DeepSeek V4.1 Flash 1017DeepSeek Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 4 | MiMo V2.6 Pro RLXiaomi Q4_K_M · 614.1 GB · 198 t/s | 198 tok/s | Q4_K_M · 614.1 GB | |
| 5 | Hy3Tencent Q4_K_M · 182.3 GB · 147 t/s | 147 tok/s | Q4_K_M · 182.3 GB |
| # | Model | Speed | Coding Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Kimi K3.1 ProMoonshot AIHow to run → Q4_K_M · 1712.1 GB · 150 t/s | 150 tok/s | Q4_K_M · 1712.1 GB | |
| 2 | DeepSeek V4.1 Flash 1017DeepSeek Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 3 | Kimi K3Moonshot AI Q4_K_M · 1670.0 GB · 151 t/s | 151 tok/s | Q4_K_M · 1670.0 GB | |
| 4 | GLM 5.3Z.ai Q4_K_M · 453.8 GB · 149 t/s | 149 tok/s | Q4_K_M · 453.8 GB | |
| 5 | Hy3Tencent Q4_K_M · 182.3 GB · 147 t/s | 147 tok/s | Q4_K_M · 182.3 GB |
| # | Model | Speed | Reasoning Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 2 | Inkling 2 SmallThinking Machines Q4_K_M · 359.1 GB · 276 t/s | 276 tok/s | Q4_K_M · 359.1 GB | |
| 3 | Kimi K3.1 ProMoonshot AI Q4_K_M · 1712.1 GB · 150 t/s | 150 tok/s | Q4_K_M · 1712.1 GB | |
| 4 | DeepSeek V4.1 FlashDeepSeek Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 5 | Hy3Tencent Q4_K_M · 182.3 GB · 147 t/s | 147 tok/s | Q4_K_M · 182.3 GB |
| # | Model | Speed | Math Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Kimi K3Moonshot AIHow to run → Q4_K_M · 1670.0 GB · 151 t/s | 151 tok/s | Q4_K_M · 1670.0 GB | |
| 2 | DeepSeek V4.1 Flash 1017DeepSeek Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 3 | DeepSeek V4.1 FlashDeepSeek Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 4 | GLM 5.3Z.ai Q4_K_M · 453.8 GB · 149 t/s | 149 tok/s | Q4_K_M · 453.8 GB | |
| 5 | Hy3Tencent Q4_K_M · 182.3 GB · 147 t/s | 147 tok/s | Q4_K_M · 182.3 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 1595 t/s | 1595 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 818 t/s | 818 tok/s | Q4_K_M · 6.6 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 550 t/s | 550 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 459 t/s | 459 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Hy3Tencent Q4_K_M · 182.3 GB · 147 t/s | 147 tok/s | Q4_K_M · 182.3 GB |
| # | Model | Speed | Agents Score | Quant · Memory |
|---|---|---|---|---|
| 1 | Q4_K_M · 458.4 GB · 320 t/s | 320 tok/s | Q4_K_M · 458.4 GB | |
| 2 | Kimi K3.1 ProMoonshot AI Q4_K_M · 1712.1 GB · 150 t/s | 150 tok/s | Q4_K_M · 1712.1 GB | |
| 3 | GLM 5.3Z.ai Q4_K_M · 453.8 GB · 149 t/s | 149 tok/s | Q4_K_M · 453.8 GB | |
| 4 | MiMo V2.6 Pro RLXiaomi Q4_K_M · 614.1 GB · 198 t/s | 198 tok/s | Q4_K_M · 614.1 GB | |
| 5 | Hy3Tencent Q4_K_M · 182.3 GB · 147 t/s | 147 tok/s | Q4_K_M · 182.3 GB |
See all 454 models on the NVIDIA DGX B300 →Bars = traction for models without a Score yet
The NVIDIA DGX B300 runs models up to 2.8T parameters, like Kimi K3.1 Pro. A 4K-token prompt waits 110 ms before the first word on Kimi K3.1 Pro. 8x B300 SXM (Blackwell Ultra) (Blackwell Ultra), released 2025.
For models2.3 TBall of it
Chip8x B300 SXM (Blackwell Ultra)released 2025
NVIDIA DGX B300 strengths and weaknesses
Strengths
- Runs 120B MoE models: GPT OSS 120B at 378 tok/s in 70.8 GB
- Room for long context: Llama 3.3 70B Instruct at Q4 uses 51.3 GB, leaving 2.3 TB free
- Reads long prompts fast: A 32K-token prompt on Kimi K3.1 Pro starts answering in 860 ms
- FP8 and NVFP4 run natively: Blackwell takes FP8 and NVFP4 checkpoints in vLLM as published
Memory for models
2.3 TB of 2.3 TB120B MoE70.8 GB2780B1712.1 GB
120B MoE70.8 GB2780B1712.1 GB
For models 2.3 TBModel size at Q4
Everything on the size ladder fits. Kimi K3.1 Pro and MiMo V2.6 Flash RL together use 1.9 TB and leave 405.4 GB free.
The same chip from other makers first, then machines and GPUs people weigh against it, each with the one reason it is worth a look. Speeds on Qwen3.6 35B A3B.
Step down to 508.5 GBMac Studio M5 Ultra (512 GB)Apple · 508.5 GB for models · M5 Ultra
▼ 76% speed▼ 1.8 TBabout $22,299
Step down to 508.5 GBMac Studio M3 Ultra (512 GB)Apple · 508.5 GB for models · M3 Ultra▼ 79% speed▼ 1.8 TBabout $9,499
Step down to 188.5 GBMac Studio M2 Ultra (192 GB)Apple · 188.5 GB for models · M2 Ultra▼ 79% speed▼ 2.1 TBabout $5,599
Step down to 124.5 GBMac Studio M4 Max (128 GB)Apple · 124.5 GB for models · M4 Max▼ 82% speed▼ 2.2 TBabout $3,499
Step down to 24 GBRTX 4090NVIDIA · 24 GB VRAM · GPU▼ 58% speed▼ 2.3 TBabout $1,599rent $0.34/hr
Step down to 32 GBRTX 5090NVIDIA · 32 GB VRAM · GPU▼ 41% speed▼ 2.3 TBabout $5,502rent $0.50/hr
Output speed depends on memory bandwidth: each model runs on as few of the 8 GPUs as hold it, at 8 TB/s per GPU. Grey rows need more than the 2.3 TB available.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB818tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB547tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB299tok/s
- 35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB363tok/s
- 70BLlama 3.3 70B Instruct · Q4_K_M · 51.3 GB112tok/s
- 120B MoEGPT OSS 120B · Q4_K_M · 70.8 GB378tok/s
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 454 models on the NVIDIA DGX B300
Featured onlyBest quant that fits in 2304 GB, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
DeepSeek V2.5 1210ChatFunctions | Q4_K_M | 142.9 GB | 172.0 t/s | 164K | S98, Runs great | |
Intern S2 Preview 397BVisionFunctions | BF16 | 809.5 GB | 175.0 t/s | 262K | S99, Runs great | |
| Q4_K_M | 18.1 GB | 343.7 t/s | 131K | S100, Runs great | ||
Qwen2.5 VL 3B InstructVision | Q4_K_M | 3.2 GB | 1977.2 t/s | 128K | S100, Runs great | |
Falcon 11BChat | Q4_K_M | 8.5 GB | 709.4 t/s | 8K | S100, Runs great | |
NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16ChatFunctions | Q4_K_M | 54.5 GB | 264.4 t/s | 262K | S100, Runs great | |
Gemma 3 12B ITVision | Q4_K_M | 10.9 GB | 646.8 t/s | 33K | S100, Runs great | |
Llama 3.2 1B InstructChatFunctions | Q4_K_M | 2.3 GB | 6341.5 t/s | 131K | S100, Runs great | |
Llama 3.2 3B InstructChatFunctions | Q4_K_M | 4.1 GB | 2452.8 t/s | 131K | S100, Runs great | |
Olmo 3 7B InstructChatFunctions | Q4_K_M | 8.4 GB | 904.3 t/s | 66K | S100, Runs great | |
WizardLM 2 8x22BChat | Q4_K_M | 88.4 GB | 130.9 t/s | 66K | S95, Runs great | |
Granite Docling 258MVisionChatCodeMath | Q4_K_M | 0.6 GB | 10400.0 t/s | 8K | S100, Runs great | |
| Q4_K_M | 17.8 GB | 349.9 t/s | 33K | S100, Runs great | ||
Gemma 3 4B ITVision | Q4_K_M | 5 GB | 1831.0 t/s | — | S100, Runs great | |
Qwen3.5 9B BaseVision | Q4_K_M | 6.6 GB | 817.6 t/s | 262K | S100, Runs great |
Specs
- Chip
- 8x B300 SXM (Blackwell Ultra) · NVIDIA B300
- CPU cores
- 128
- GPU cores
- 163,840
- Memory
- 2.3 TB
- For models
- 2.3 TB
- Memory bandwidth
- 64 TB/s
- FP16 (dense)
- 18 PFLOPS
- Power
- 15 kW max
- Size
- 10U
- Networking
- 8x ConnectX-8 (800 Gb/s)
- Released
- 2025-09-01
NVIDIA DGX B300 FAQ
How much memory is available for AI on the NVIDIA DGX B300?
All 2.3 TB can hold model weights.
What is the largest model the NVIDIA DGX B300 can run?
Kimi K3.1 Pro (2780.1B) is the largest at 4-bit or better: at Q4_K_M it uses 1712.1 GB, 74% of the available memory, and runs at about 150 tok/s.
How long does the NVIDIA DGX B300 take to read a long prompt?
On Kimi K3.1 Pro, about 110 ms for a 4K-token prompt and 860 ms for a 32K-token document before the first word appears.