gpt-oss-120b on MetaX C500 64GB (沐曦曦云) (64GB) — VRAM, KV Cache & Throughput
gpt-oss-120b (116.83B total, 5.1B active, Sparse MoE + GQA) uses 58.46 GiB of 4-bit weights (Q4_K_M), requiring 59.31 GiB total at 8K context and 60.15 GiB at 32K on MetaX C500 64GB (沐曦曦云) (~191.8 tok/s decode).
Fits with Safe Headroom
Measured GGUF shard size
HF config.json ↗
Required GPU Memory
59.31 GiB
63.68 GB (decimal)
Usable Hardware Memory
60.8 GiB
64 GB (physical)
Safe Headroom
+1.49 GiB
93% utilization
Est. Decode Throughput
~191.8 tok/s
5.1B active params
Est. Prefill Throughput
~13051 tok/s
4K Prompt TTFT: ~314 ms
Est. Cost / 1M Tokens
$0.64
Sparse MoE + GQA · Apache-2.0
Recommended Hardware for Current Configuration
Best Single GPU
RTX PRO 6000 Blackwell (96GB)
~$8,500
Best Value / Used GPU
2x A100 80GB SXM
160GB · ~$24,000
Apple Unified Memory
Ryzen AI Max+ 395 128GB
~$1,999
Cloud From
Vast.ai · $0.44 / hr
1x A100 80GB SXM
Production Launch Commands
llama.cpp (GGUF)
llama-server -hf ggml-org/gpt-oss-120b-GGUF:Q4_K_M \ --ctx-size 8192 -ngl 999 --flash-attn
vLLM
vllm serve openai/gpt-oss-120b \ --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~314ms · ~191.8 tok/s)
[gpt-oss-120b · 59.31 GiB] allocated on target GPU. Prefill completed in TTFT window; streaming autoregressive tokens at memory-bandwidth-bound rate...
Cloud GPU Comparison & Rent-vs-Buy TCO
Verified public rates across RunPod, Vast.ai, Vultr, and DigitalOcean.
Purchase break-even
~64.4 months
Est. local power: $12.77 / mo
| Provider | Recommended Instance | Total VRAM | Hourly Rate | Monthly at Selected Hours | Billing Mode | Deploy |
|---|---|---|---|---|---|---|
| RunPod | 1x A100 80GB SXM (Community Cloud · PCIe) | 80 GB | $1.19 / hr | $144.70 / mo | Per-second | Deploy GPU ↗ |
| Vast.ai | 1x A100 80GB SXM (Verified Host · SXM4) | 80 GB | $0.44 / hr | $53.75 / mo | Spot / On-demand | Deploy GPU ↗ |
| Vultr ($300 Free Credit*) | 1x A100 80GB SXM (Dedicated Instance · $300 Credit*) | 80 GB | $2.40 / hr | $291.48 / mo | Hourly | Deploy GPU ↗ |
| Vultr Cloud GPU | 1x A100 80GB SXM (Dedicated Instance) | 80 GB | $2.40 / hr | $291.48 / mo | Hourly | Deploy GPU ↗ |
| DigitalOcean | 1x Instinct MI300X 192GB (Dedicated Instance) | 192 GB | $2.59 / hr | $314.94 / mo | Per-second | Deploy GPU ↗ |
Rates reflect public listings on the verification date and fluctuate with regional inventory.
Fine-Tuning VRAM Requirements (QLoRA / LoRA / Full) — gpt-oss-120b
QLoRA (4b)
Tight Fit (<8% Headroom)
60.7 GiB
Model Weights: 58.46 GiB · Gradients + Optimizer: 1.2 GiB · Act: 1.04 GiB
Recommended GPU: RTX PRO 6000 Blackwell (96GB)
LoRA (16b)
Exceeds Available GPU Memory
64.06 GiB
Model Weights: 60.88 GiB · Gradients + Optimizer: 1.8 GiB · Act: 1.38 GiB
Recommended GPU: RTX PRO 6000 Blackwell (96GB)
Full BF16
Exceeds Available GPU Memory
252.04 GiB
Model Weights: 60.88 GiB · Gradients + Optimizer: 188.73 GiB · Act: 2.43 GiB
Recommended GPU: Instinct MI355X 288GB (288GB)
Engineering Sizing Notes & Optimization Playbook
Quick Hardware Verdict
MetaX C500 64GB (沐曦曦云) (64GB physical, 60.8 GiB usable) runs 4-bit (Q4_K_M) gpt-oss-120b at 8K context using 59.31 GiB with 1.49 GiB of safe headroom (~191.8 tok/s single-stream decode).
KV Cache & Runtime Tuning
gpt-oss-120b uses Sparse MoE + GQA (116.83B total, 5.1B active per token). Moving from 8K to 32K context expands FP16 KV cache from 0.29 GiB to 1.13 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 0.57 GiB at 32K (reducing total VRAM to 59.58 GiB).
VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)
Quantization Precision & Memory Matrix — gpt-oss-120b @ 8K
| Quantization Format | Weights (GiB) | KV Cache | Total VRAM (GiB) | Decode Speed | Compatibility (MetaX C500 64GB (沐曦曦云)) | Recommended GPU | Data Source |
|---|---|---|---|---|---|---|---|
| Native Weights (BF16 / FP8 / MXFP4 — Lossless) (MXFP4) | 60.88 GiB | 0.29 GiB | 61.73 GiB (66.28 GB) | ~187.4 tok/s | Tight Fit (<8% Headroom) | RTX PRO 6000 Blackwell (96GB) | Official checkpoint size |
| FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) | 59.03 GiB | 0.29 GiB | 59.88 GiB (64.3 GB) | ~190.7 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
| GGUF Q6_K (6-bit — High Precision) (Q6_K) | 58.94 GiB | 0.29 GiB | 59.79 GiB (64.2 GB) | ~190.9 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
| GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) | 58.57 GiB | 0.29 GiB | 59.42 GiB (63.8 GB) | ~191.6 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
| Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) | 58.46 GiB | 0.29 GiB | 59.31 GiB (63.68 GB) | ~191.8 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
| GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) | 58.4 GiB | 0.29 GiB | 59.25 GiB (63.62 GB) | ~191.9 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
| GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) | 58.33 GiB | 0.29 GiB | 59.18 GiB (63.54 GB) | ~192 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
| GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) | 57.9 GiB | 0.29 GiB | 58.75 GiB (63.08 GB) | ~192.8 tok/s | Fits with Safe Headroom | RTX PRO 6000 Blackwell (96GB) | Measured GGUF shard size |
Technical Specifications & FAQ
Does gpt-oss-120b fit on MetaX C500 64GB (沐曦曦云) (64GB)?
In 4-bit (Q4_K_M) at 8,192 context, gpt-oss-120b requires 59.31 GiB total GPU memory (58.46 GiB weights + 0.29 GiB KV cache + 0.56 GiB runtime buffers) against 60.8 GiB usable on MetaX C500 64GB (沐曦曦云), delivering ~191.8 tok/s decode.
How much VRAM does gpt-oss-120b need at 32K context?
At 32,768 context tokens, gpt-oss-120b (Sparse MoE + GQA) allocates 1.13 GiB for KV cache and state, bringing total 4-bit VRAM to 60.15 GiB (64.59 GB decimal).
Evaluate This Model on Other GPUs
RTX 3060 12GB (12GB)RTX 4060 Ti 16GB (16GB)RTX 5060 Ti 16GB (16GB)RTX 4070 Ti Super (16GB)RTX 4080 Super (16GB)RTX 5070 12GB (12GB)RTX 5070 Ti (16GB)RTX 5080 (16GB)RTX 3090 (24GB)RTX 4090 (24GB)RTX 4090 D (24GB)RTX 5090 (32GB)RTX 5090 D (32GB)RTX A6000 (48GB)RTX 6000 Ada (48GB)RTX PRO 6000 Blackwell (96GB)DGX Spark (GB10 128GB) (128GB)L4 24GB (24GB)A40 48GB (48GB)L20 48GB (48GB)L40S 48GB (48GB)A800 80GB SXM (80GB)A100 80GB SXM (80GB)H800 80GB SXM (80GB)H100 80GB SXM (80GB)H20 96GB HBM3 (96GB)H20 141GB HBM3e (141GB)H200 141GB SXM (141GB)B200 180GB (180GB)B300 288GB Blackwell Ultra (288GB)Ascend 910B 64GB (昇腾) (64GB)Ascend 910C 128GB (昇腾) (128GB)Atlas 300I Duo 96GB (310P) (96GB)MLU590 80GB (寒武纪思元) (80GB)DCU K100-AI 64GB (海光深算) (64GB)Kunlunxin P800 64GB (昆仑芯) (64GB)MTT S4000 48GB (摩尔线程) (48GB)Radeon RX 7900 XTX 24GB (24GB)Radeon AI PRO R9700 32GB (32GB)Ryzen AI Max+ 395 128GB (128GB)Instinct MI300X 192GB (192GB)Instinct MI325X 256GB (256GB)Instinct MI355X 288GB (288GB)Arc Pro B60 24GB (24GB)Gaudi 3 128GB HBM2e (128GB)Mac mini M4 Pro 64GB (64GB)MacBook Pro M4 Max 128GB (128GB)Mac Studio M4 Ultra 256GB (256GB)Mac Studio M3 Ultra 512GB (512GB)
Evaluate Other Models on This Hardware
Qwen3.8 Flash Next (125B-A6B)Qwen3.8 27BQwen3.8 2.4T-A95BQwen3.6 35B-A3BQwen3.5 397B-A17BQwen3.5 122B-A10BQwen3.5 35B-A3BQwen3.5 27BQwen3.5 9BQwen3.5 4BQwen3 Coder 480B-A35BQwen3 Coder Next 80B-A3BQwen3 Coder 30B-A3BQwen3 30B-A3B Instruct 2507Qwen3 235B-A22B Instruct 2507Qwen3 32BQwen3 14BQwen3 8BQwen3 4B Instruct 2507Qwen3 1.7BQwen3 0.6BQwen2.5 72B InstructQwen2.5 Coder 32B InstructQwQ 32B ReasoningDeepSeek V4.1 Flash (552B-A16B)DeepSeek V4 FlashDeepSeek V3.2 (685B)DeepSeek R1-0528 671BDeepSeek V3-0324 671BDeepSeek R1 Distill Llama 70BDeepSeek R1 Distill Qwen 32BDeepSeek R1 Distill Qwen 14BDeepSeek R1 Distill Llama 8Bgpt-oss-20bGemma 4 31B ITGemma 4 26B-A4B ITGemma 4 12B ITGemma 4 E4B ITGemma 4 E2B ITGemma 3 27B ITGemma 3 12B ITGemma 3 4B ITGLM-5.3 Flash (320B-A18B)GLM-5.3 (753B)GLM-5 (744B)GLM-4.5 (355B-A32B)GLM-4.5 Air (110B-A12B)GLM-4 32B-0414Kimi K3.1 Pro (2.8T-A104B)Kimi K3 (2.8T-A104B)Kimi K2 Instruct (1T-A32B)Kimi-Dev 72BMiniMax M3.1 Flash (428B-A23B)MiniMax M2.5 (229B-A10B)Step-5 Preview (600B-A27B)Step-3.7 Flash (198B-A11B)Step-3.5 Flash (197B-A11B)ERNIE 4.5 300B-A47B (文心)ERNIE 4.5 21B-A3B (文心)Hunyuan A13B Instruct (混元 80B)MiniCPM5 2B (面壁)Llama 4 Maverick 17B-128E (400B)Llama 4 Scout 17B-16E (109B)Llama 3.1 405B InstructLlama 3.3 70B InstructLlama 3.1 8B InstructLlama 3.2 3B InstructMistral Large 4 (1T-A52B)Mistral Large 2 123BMixtral 8x22B InstructMixtral 8x7B InstructMistral Small 3.2 24BDevstral Small 24BNemotron 3 Ultra 550B-A55BNemotron 3 Super 120B-A12BNemotron 3 Nano 30B-A3BCohere Command A 111BPhi-4 14BLiquid LFM3 1.7B