gpt-oss-120b on RTX 5090 D (32GB) — 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 RTX 5090 D (~30.7 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →

Open-Weight Model116.83B / 5.1B

Weights & QuantizationQ4_K_M · 58.46 GiB

Context Window8,192 (8K)

Target Hardware

32 GB · 1792 GB/s

GPU Count (Tensor Parallel)

Concurrent Batch Size

KV Cache Precision

Inference Engine

System RAM Offload (CPU)0%

Exceeds Available GPU Memory

Measured GGUF shard size HF config.json ↗

Memory Allocation Breakdown (GiB) 59.31 GiB (63.68 GB) / 32 GiB (185%)

Model Weights: 58.46 GiB KV Cache & State: 0.29 GiB Runtime & Activation Buffers: 0.56 GiB System RAM Offload: 0 GiB

Required GPU Memory

59.31 GiB

63.68 GB (decimal)

Usable Hardware Memory

30.4 GiB

32 GB (physical)

Safe Headroom

-28.91 GiB

185% utilization

Est. Decode Throughput

~30.7 tok/s

5.1B active params

Est. Prefill Throughput

~9431 tok/s

4K Prompt TTFT: ~434 ms

Est. Cost / 1M Tokens

$4.00

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 ~434ms · ~30.7 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.

Verified: 2026-11-04

Active usage (hours / day)4 h / day

Local hardware budget ($)

Electricity rate ($/kWh)

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) Exceeds Available GPU Memory

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

A single RTX 5090 D (32GB) cannot hold 4-bit gpt-oss-120b (59.31 GiB at 8K) entirely in VRAM. Use multi-GPU tensor parallelism, offload layers to system RAM, or deploy on a larger cloud GPU.

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)

Model weights remain constant while attention KV cache grows with context length and batch size.

Quantization Precision & Memory Matrix — gpt-oss-120b @ 8K

Quantization Format	Weights (GiB)	KV Cache	Total VRAM (GiB)	Decode Speed	Compatibility (RTX 5090 D)	Recommended GPU	Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (MXFP4)	60.88 GiB	0.29 GiB	61.73 GiB (66.28 GB)	~28.7 tok/s	Exceeds Available GPU Memory	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)	~30.2 tok/s	Exceeds Available GPU Memory	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)	~30.3 tok/s	Exceeds Available GPU Memory	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)	~30.6 tok/s	Exceeds Available GPU Memory	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)	~30.7 tok/s	Exceeds Available GPU Memory	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)	~30.7 tok/s	Exceeds Available GPU Memory	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)	~30.8 tok/s	Exceeds Available GPU Memory	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)	~31.2 tok/s	Exceeds Available GPU Memory	RTX PRO 6000 Blackwell (96GB)	Measured GGUF shard size

Technical Specifications & FAQ

Does gpt-oss-120b fit on RTX 5090 D (32GB)?

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 30.4 GiB usable on RTX 5090 D, delivering ~30.7 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 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 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)MetaX C500 64GB (沐曦曦云) (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)

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