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Calculator › Step-5 Preview (600B-A27B) › RTX 5080

Step-5 Preview (600B-A27B) on RTX 5080 (16GB) — VRAM, KV Cache & Throughput

Step-5 Preview (600B-A27B) (600B total, 27B active, Sparse MoE + GQA) uses 338.98 GiB of 4-bit weights (Q4_K_M), requiring 340.18 GiB total at 8K context and 341.8 GiB at 32K on RTX 5080 (~3.4 tok/s decode).

Interactive Sizing Workbench

Full Compatibility Report →
16 GB · 960 GB/s
Exceeds Available GPU Memory
Config-derived formula HF config.json ↗
Memory Allocation Breakdown (GiB) 340.18 GiB (365.27 GB) / 16 GiB (2126%)
Model Weights: 338.98 GiB KV Cache & State: 0.64 GiB Runtime & Activation Buffers: 0.56 GiB System RAM Offload: 0 GiB
Required GPU Memory
340.18 GiB
365.27 GB (decimal)
Usable Hardware Memory
15.2 GiB
16 GB (physical)
Safe Headroom
-324.98 GiB
2126% utilization
Est. Decode Throughput
~3.4 tok/s
27B active params
Est. Prefill Throughput
~1088 tok/s
4K Prompt TTFT: ~3764 ms
Est. Cost / 1M Tokens
$288.89
Sparse MoE + GQA · Custom

Recommended Hardware for Current Configuration

Best Single GPU
Multi-GPU Required
—
Best Value / Used GPU
4x H200 141GB SXM
564GB · ~$128,000
Apple Unified Memory
Mac Studio M3 Ultra 512GB
~$9,500
Cloud From
Vast.ai · $3.54 / hr
8x A100 80GB SXM

Production Launch Commands

llama.cpp (GGUF) 
llama-server -hf stepfun-ai/Step-5-Preview-GGUF:Q4_K_M \
  --ctx-size 8192 -ngl 999 --flash-attn
vLLM 
vllm serve stepfun-ai/Step-5-Preview \
  --max-model-len 8192 --gpu-memory-utilization 0.92
Live Token Speed Simulator (TTFT ~3764ms · ~3.4 tok/s)
[Step-5 Preview (600B-A27B) · 340.18 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: 2026-11-05
Purchase break-even
~0.7 months
Est. local power: $7.66 / mo
Provider Recommended Instance Total VRAM Hourly Rate Monthly at Selected Hours Billing Mode Deploy
RunPod 2x B300 288GB Blackwell Ultra (Community Cloud) 576 GB $13.88 / hr $1687.81 / mo Per-second Deploy GPU ↗
Vast.ai 8x A100 80GB SXM (Verified Host · SXM4) 640 GB $3.54 / hr $429.98 / mo Spot / On-demand Deploy GPU ↗
Vultr ($300 Free Credit*) 8x A100 80GB SXM (Dedicated Instance · $300 Credit*) 640 GB $19.18 / hr $2331.80 / mo Hourly Deploy GPU ↗
Vultr Cloud GPU 8x A100 80GB SXM (Dedicated Instance) 640 GB $19.18 / hr $2331.80 / mo Hourly Deploy GPU ↗
DigitalOcean 8x Instinct MI300X 192GB (Dedicated Instance) 1536 GB $20.72 / hr $2519.55 / 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) — Step-5 Preview (600B-A27B)

QLoRA (4b) Exceeds Available GPU Memory
343.84 GiB
Model Weights: 338.98 GiB · Gradients + Optimizer: 3.78 GiB · Act: 1.08 GiB
Recommended GPU: 4x H200 141GB SXM (564GB)
LoRA (16b) Exceeds Available GPU Memory
1124.96 GiB
Model Weights: 1117.59 GiB · Gradients + Optimizer: 5.94 GiB · Act: 1.43 GiB
Recommended GPU: 8x B200 180GB (1440GB)
Full BF16 Exceeds Available GPU Memory
4584.64 GiB
Model Weights: 1117.59 GiB · Gradients + Optimizer: 3464.53 GiB · Act: 2.52 GiB
Recommended GPU: Multi-GPU Required

Engineering Sizing Notes & Optimization Playbook

Quick Hardware Verdict

A single RTX 5080 (16GB) cannot hold 4-bit Step-5 Preview (600B-A27B) (340.18 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

Step-5 Preview (600B-A27B) uses Sparse MoE + GQA (600B total, 27B active per token). Moving from 8K to 32K context expands FP16 KV cache from 0.64 GiB to 2.26 GiB; enabling 8-bit KV cache (llama.cpp -ctk q8_0 -ctv q8_0 or vLLM --kv-cache-dtype fp8) saves 1.13 GiB at 32K (reducing total VRAM to 340.67 GiB).

VRAM Scaling Across Context Windows (4K to 128K, 4-bit Weights)

Step-5 Preview (600B-A27B) VRAM scaling from 4K to 128K context (4K: 339.91 GiB, 8K: 340.18 GiB, 32K: 341.8 GiB, 128K: 348.27 GiB) 4K 339.91 GiB (KV 0.37 GiB) 8K 340.18 GiB (KV 0.64 GiB) 32K 341.8 GiB (KV 2.26 GiB) 128K 348.27 GiB (KV 8.73 GiB) RTX 5080 (16GB)
Model weights remain constant while attention KV cache grows with context length and batch size.

Quantization Precision & Memory Matrix — Step-5 Preview (600B-A27B) @ 8K

Quantization Format Weights (GiB) KV Cache Total VRAM (GiB) Decode Speed Compatibility (RTX 5080) Recommended GPU Data Source
Native Weights (BF16 / FP8 / MXFP4 — Lossless) (BF16) 1117.59 GiB 0.64 GiB 1118.79 GiB (1201.29 GB) ~1.1 tok/s Exceeds Available GPU Memory Multi-GPU Required Official checkpoint size
FP8 / GGUF Q8_0 (8-bit — Near-Lossless) (Q8_0) 593.72 GiB 0.64 GiB 594.92 GiB (638.79 GB) ~2 tok/s Exceeds Available GPU Memory Multi-GPU Required Config-derived formula
GGUF Q6_K (6-bit — High Precision) (Q6_K) 458.21 GiB 0.64 GiB 459.41 GiB (493.29 GB) ~2.5 tok/s Exceeds Available GPU Memory Multi-GPU Required Config-derived formula
GGUF Q5_K_M (5-bit — Balanced) (Q5_K_M) 397.55 GiB 0.64 GiB 398.75 GiB (428.15 GB) ~2.9 tok/s Exceeds Available GPU Memory Multi-GPU Required Config-derived formula
Q4_K_M / UD-Q4_K_XL / AWQ (4-bit — Recommended) (Q4_K_M) 338.98 GiB 0.64 GiB 340.18 GiB (365.27 GB) ~3.4 tok/s Exceeds Available GPU Memory Multi-GPU Required Config-derived formula
GGUF IQ4_XS (4.25-bit — Compact) (IQ4_XS) 297.01 GiB 0.64 GiB 298.21 GiB (320.2 GB) ~3.8 tok/s Exceeds Available GPU Memory Multi-GPU Required Config-derived formula
GGUF Q3_K_M (3-bit — Low VRAM) (Q3_K_M) 273.24 GiB 0.64 GiB 274.44 GiB (294.68 GB) ~4.2 tok/s Exceeds Available GPU Memory Multi-GPU Required Config-derived formula
GGUF UD-Q2_K_XL (2-bit — Extreme MoE) (UD-Q2_K_XL) 192.3 GiB 0.64 GiB 193.5 GiB (207.77 GB) ~5.9 tok/s Exceeds Available GPU Memory Instinct MI325X 256GB (256GB) Config-derived formula

Technical Specifications & FAQ

Does Step-5 Preview (600B-A27B) fit on RTX 5080 (16GB)?

In 4-bit (Q4_K_M) at 8,192 context, Step-5 Preview (600B-A27B) requires 340.18 GiB total GPU memory (338.98 GiB weights + 0.64 GiB KV cache + 0.56 GiB runtime buffers) against 15.2 GiB usable on RTX 5080, delivering ~3.4 tok/s decode.

How much VRAM does Step-5 Preview (600B-A27B) need at 32K context?

At 32,768 context tokens, Step-5 Preview (600B-A27B) (Sparse MoE + GQA) allocates 2.26 GiB for KV cache and state, bringing total 4-bit VRAM to 341.8 GiB (367 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 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)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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