StepFun, China · Released February 1, 2026Step 3.5 Flash at a glance
- Made by
- StepFun, China
- Released
- February 1, 2026
- Parameters
- 199.4B, 11B active
- Context window
- 256K tokens
Step 3.5 Flash VRAM Requirements
StepFun, China · Released February 1, 2026
Step 3.5 Flash has 199.4B parameters (11B active per token). In BF16 its weights alone take 367 GiB; quantized to 4-bit, about 111 GiB. Each 32K-token request adds 1.56 GiB of KV cache. Cheapest way to run it today: 6 × GeForce RTX 3090 (24 GB) at $0.846 per hour.
Size your own setup in the calculator →VRAM by precision and context length
Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.
| Weights format | Weights only | + 8K context | + 32K context | + 128K context | + 256K context |
|---|---|---|---|---|---|
| BF16 / FP16 | 367 | 367 | 368 | 373 | 379 |
| FP8 / INT8 | 184 | 185 | 186 | 190 | 196 |
| 4-bit (GGUF Q4_K_M) | 111 | 111 | 112 | 117 | 123 |
How many GPUs to run Step 3.5 Flash
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
| GPU | BF16 / FP16 | 8-bit | 4-bit |
|---|---|---|---|
| GeForce RTX 4090 24 GB | 24 (several machines) | 16 (several machines) | 6 |
| GeForce RTX 5090 32 GB | 16 (several machines) | 7 | 4 |
| RTX PRO 6000 Blackwell 96 GB | 4 | 2 | 2 |
| Mac M5 Max 128 GB 96 GB usable | 4 | 2 | 2 |
| Ryzen AI Max+ 395 128 GB 96 GB usable | 4 | 2 | 2 |
| DGX Spark (GB10) 128 GB | 3 | 2 | 1 |
| NVIDIA H100 80 GB | 6 | 3 | 2 |
| NVIDIA H200 141 GB | 4 | 2 | 1 |
| NVIDIA B200 (HGX) 180 GB | 3 | 2 | 1 |
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.
Cheapest way to run Step 3.5 Flash today
For each GPU: the fewest cards that fit Step 3.5 Flash with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-16).
| Setup | Per hour | Weights | Provider | Per month | Rent |
|---|---|---|---|---|---|
| 6 × GeForce RTX 3090 24 GB | $0.846 | Q4_K_M | Vast.ai | $617.58 | Rent 6 × GeForce RTX 3090 on Vast.ai (opens in a new tab) → |
| 6 × RTX A5000 24 GB | $0.966 | Q4_K_M | Vast.ai | $705.18 | Rent 6 × RTX A5000 on Vast.ai (opens in a new tab) → |
| 3 × RTX A6000 48 GB | $1.08 | Q4_K_M | Vast.ai | $790.59 | Rent 3 × RTX A6000 on Vast.ai (opens in a new tab) → |
| 6 × RTX PRO 4000 Blackwell 24 GB | $1.39 | Q4_K_M | Vast.ai | $1,016.16 | Rent 6 × RTX PRO 4000 Blackwell on Vast.ai (opens in a new tab) → |
| 4 × GeForce RTX 5090 32 GB | $1.60 | Q4_K_M | Vast.ai | $1,168.00 | Rent 4 × GeForce RTX 5090 on Vast.ai (opens in a new tab) → |
| 7 × RTX A4500 20 GB | $1.75 | Q4_K_M | RunPod | $1,277.50 | Rent 7 × RTX A4500 on RunPod (opens in a new tab) → |
| 6 × GeForce RTX 4090 24 GB | $1.80 | Q4_K_M | Akash Network | $1,314.00 | Rent 6 × GeForce RTX 4090 on Akash Network (opens in a new tab) → |
| 4 × NVIDIA A100 40GB | $1.82 | 4-bit | Vast.ai | $1,328.60 | Rent 4 × NVIDIA A100 40GB on Vast.ai (opens in a new tab) → |
On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.
KV cache: how memory grows with context
Architecture: MoE 288 experts + 1 shared · 12 full-attention layers + 33 sliding-window layers (512 tokens), GQA 8 KV × 128. Its KV cache is 3.6× smaller than if every layer used full attention, because most layers only keep a short window of recent tokens. Only 11B parameters are active per token, but all 199B must sit in memory: VRAM depends on the total, speed on the active part.
| Context per request | BF16 cache | FP8 cache |
|---|---|---|
| 8K tokens | 0.44 GiB | 0.22 GiB |
| 32K tokens | 1.56 GiB | 0.78 GiB |
| 128K tokens | 6.06 GiB | 3.03 GiB |
| 256K tokens | 12.1 GiB | 6.03 GiB |
Step 3.5 Flash VRAM FAQ
How much VRAM does Step 3.5 Flash need?
In BF16 the weights alone take 367 GiB (394 GB). In FP8 that is 184 GiB, and about 111 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 0.44 GiB at 8K tokens and 1.56 GiB at 32K (BF16 cache).
Can Step 3.5 Flash run on a single RTX 4090 (24 GB)?
No. Even in 4-bit it needs 6 RTX 4090s. In 4-bit it fits on a single DGX Spark (GB10) (128 GB).
Can Step 3.5 Flash run on an RTX 5090 (32 GB)?
No. Even in 4-bit it needs 4 RTX 5090s.
Can Step 3.5 Flash run on a Mac?
Yes. In 4-bit (Q4_K_M with llama.cpp) it needs a Mac with 192 GB of unified memory or more, of which macOS gives the GPU about 144 GB, for one request with an 8K-token context. In 8-bit (Q8_0) it needs 256 GB or more.
Can Step 3.5 Flash run on a DGX Spark or a Ryzen AI Max+ 395 (128 GB)?
Yes. On a DGX Spark (128 GB) it fits in Q4_K_M (111 GiB with an 8K-token context); on a Ryzen AI Max+ 395 with 128 GB, whose GPU gets up to 96 GB, it does not fit even in 4-bit, with llama.cpp and one request.
What is the cheapest way to run Step 3.5 Flash?
On 2026-10-16, the cheapest on-demand setup is 6 × GeForce RTX 3090 (24 GB) with Q4_K_M weights on Vast.ai, at $0.846 per hour (about $617.58 per month). Next: 6 × RTX A5000 (24 GB) with Q4_K_M weights on Vast.ai, at $0.966 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Step 3.5 Flash need?
With one request and an 8K-token context: 6 in BF16, 3 in FP8 and 2 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Step 3.5 Flash VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/step-3-5-flash.json.
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Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.