JevK5 — Hardware Requirements & GPU Compatibility
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JevK5 is a 4.2B-parameter open language model from alibiserikbay. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.99 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 128K)Best on 24 GB (Q4_K_M · ~219 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~24 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run JevK5
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/alibiserikbay/JevK5-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 20.8 GB free · 16K context
Download: jevk5-4b-v0.2-Q4_K_M.gguf2.71 GBalibiserikbay
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~78 tok/s
- generation speed
- Cost per 1M output tokens
- $0.25
- per 1M output tokens
How Much VRAM Does JevK5 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.3 GB | 2.8 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~290 tok/s | |
| 2.5 GB | 3 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~260 tok/s | |
| 3.0 GB | 3.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~219 tok/s | |
| 3.5 GB | 4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~189 tok/s | |
| 3.9 GB | 4.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~166 tok/s | |
| 4.7 GB | 5.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~140 tok/s | |
| 8.9 GB | 9.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~74 tok/s |
Selected Q4_K_M
4-bit medium quantization3.2GBat 16K context
- Weights
- 2.5 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.8 GB free · ~219 tok/s
≥50 tok/s needs ≥230 GB/s · RTX 5090 ~390, RTX 3090 Ti ~219
Which Hardware Can Run JevK5?
Q4_K_M · 3.0 GBJevK5 (Q4_K_M) requires 3.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 5.2 GB, bringing total usage to 8.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run JevK5, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.8 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~165Simulate output on RTX 3080RTX 2080 Ti~134Simulate output on RTX 2080 TiRTX 3070 Ti~132Simulate output on RTX 3070 TiM3*8 GB~24Simulate output on iMac 24" M3 (8 GB)Orin Nano~22Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~16Simulate output on iPhone 1720 GB
fits · 16.8 GB free · up to 256K ctx
1 GPU
RX 7900 XT~161Simulate output on RX 7900 XT32–40 GB
fits · 28.8 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~390Simulate output on RTX 5090M5 Max36 GB~108Simulate output on Mac Studio M5 Max (36 GB)M532 GB~36Simulate output on MacBook Air 13" M5 (32 GB)Orin~45Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~29Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | same size |
| NVIDIA Nemotron Labs Dory1.0 4B BF16NVIDIA | — | 5.7 GB | ~214 | same size |
| Granite Vision 4.1 4BIBM | — | 4 GB | ~228 | same size |
| Translategemma 4B ITGoogle | — | 5 GB | ~231 | same size |
| Qwen3 VL 4B InstructAlibaba | — | 4.5 GB | ~208 | same size |
| MiniCPM V 4OpenBMB | — | 3.1 GB | ~236 | same size |
Specifications
- Publisher
- alibiserikbay
- Parameters
- 4.2B
- Architecture
- Qwen3_5ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-22
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does JevK5 need?
JevK5 requires 3.0 GB of VRAM at Q4_K_M, or 8.9 GB at BF16. Full 262K context adds up to 5.2 GB (8.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.2B × 4.8 bits ÷ 8 = 2.5 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 5.7 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for JevK5?
For JevK5, Q4_K_M (3.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run JevK5 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run JevK5. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run JevK5 locally?
Yes — JevK5 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is JevK5?
At Q4_K_M, JevK5 can reach ~1605 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~219 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 2.99 × 0.65 = ~1739 tok/s
Estimated speed at Q4_K_M (3.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of JevK5?
At Q4_K_M, the download is about 2.52 GB. The full-precision BF16 version is 8.41 GB. The smallest option (Q2_K) is 1.79 GB.
Which GPUs can run JevK5?
52 consumer GPUs can run JevK5 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run JevK5?
163 devices with unified memory can run JevK5 at Q4_K_M (3.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.