Jan V3.5 4B — Hardware Requirements & GPU Compatibility
ChatMath
Jan V3.5 4B is a 4.4B-parameter open language model from janhq. It supports a context window of up to 262,144 tokens. At BF16 it needs about 9.31 GB of VRAM — see which GPUs and Macs can run it below.
393 downloads
16 likesSpecifications
- Publisher
- janhq
- Parameters
- 4.4B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-03-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Jan V3.5 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 9.3 GB | 33.3 GB | 8.82 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Jan V3.5 4B?
BF16 · 9.3 GBJan V3.5 4B (BF16) requires 9.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 262K context window can add up to 24.0 GB, bringing total usage to 33.3 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Jan V3.5 4B?
BF16 · 9.3 GB27 devices with unified memory can run Jan V3.5 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Jan V3.5 4B need?
Jan V3.5 4B requires 9.3 GB of VRAM at BF16. Full 262K context adds up to 24.0 GB (33.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.4B × 16 bits ÷ 8 = 8.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 24.5 GB (at full 262K context)
VRAM usage by quantization
BF169.3 GBBF16 + full context33.3 GB- Can I run Jan V3.5 4B on a Mac?
Jan V3.5 4B requires at least 9.3 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Jan V3.5 4B locally?
Yes — Jan V3.5 4B can run locally on consumer hardware. At BF16 quantization it needs 9.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Jan V3.5 4B?
At BF16, Jan V3.5 4B can reach ~313 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~70 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 9.3 × 0.55 = ~313 tok/s
Estimated speed at BF16 (9.3 GB)
~313 tok/s~70 tok/s~234 tok/s~194 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Jan V3.5 4B?
At BF16, the download is about 8.82 GB.
- Which GPUs can run Jan V3.5 4B?
28 consumer GPUs can run Jan V3.5 4B at BF16 (9.3 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Jan V3.5 4B?
27 devices with unified memory can run Jan V3.5 4B at BF16 (9.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.