Qwen3 4B Hindi Instruct v2 — Hardware Requirements & GPU Compatibility
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Qwen3 4B Hindi Instruct v2 is a 4.0B-parameter open language model from pankajpandey-dev in the Qwen family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 2.90 GB of VRAM — see which GPUs and Macs can run it below.
102 downloads
4 likesSpecifications
- Publisher
- pankajpandey-dev
- Family
- Qwen
- Parameters
- 4.0B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-05-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3 4B Hindi Instruct v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.2 GB | 26.2 GB | 1.71 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.3 GB | 26.2 GB | 1.76 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.5 GB | 26.4 GB | 1.96 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.5 GB | 26.5 GB | 2.01 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.9 GB | 26.9 GB | 2.41 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.4 GB | 27.3 GB | 2.87 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.8 GB | 27.8 GB | 3.32 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.5 GB | 28.5 GB | 4.02 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 4B Hindi Instruct v2?
Q4_K_M · 2.9 GBQwen3 4B Hindi Instruct v2 (Q4_K_M) requires 2.9 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 24.0 GB, bringing total usage to 26.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3 4B Hindi Instruct v2?
Q4_K_M · 2.9 GB33 devices with unified memory can run Qwen3 4B Hindi Instruct v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 4B Hindi Instruct v2 need?
Qwen3 4B Hindi Instruct v2 requires 2.9 GB of VRAM at Q4_K_M, or 4.5 GB at Q8_0. Full 262K context adds up to 24.0 GB (26.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 4.8 bits ÷ 8 = 2.4 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
Q4_K_M2.9 GBQ4_K_M + full context26.9 GB- What's the best quantization for Qwen3 4B Hindi Instruct v2?
For Qwen3 4B Hindi Instruct v2, Q4_K_M (2.9 GB) offers the best balance of quality and VRAM usage. Q5_0 (3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 1.6 GB.
VRAM requirement by quantization
IQ2_XXS1.6 GBQ3_K_S2.3 GBIQ4_NL2.8 GBQ4_K_M ★2.9 GBQ5_03.0 GBQ8_04.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 4B Hindi Instruct v2 on a Mac?
Qwen3 4B Hindi Instruct v2 requires at least 1.6 GB at IQ2_XXS, 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 Qwen3 4B Hindi Instruct v2 locally?
Yes — Qwen3 4B Hindi Instruct v2 can run locally on consumer hardware. At Q4_K_M quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 4B Hindi Instruct v2?
At Q4_K_M, Qwen3 4B Hindi Instruct v2 can reach ~1005 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~226 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 ÷ 2.9 × 0.55 = ~1005 tok/s
Estimated speed at Q4_K_M (2.9 GB)
~1005 tok/s~226 tok/s~751 tok/s~622 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 4B Hindi Instruct v2?
At Q4_K_M, the download is about 2.41 GB. The full-precision Q8_0 version is 4.02 GB. The smallest option (IQ2_XXS) is 1.11 GB.
- Which GPUs can run Qwen3 4B Hindi Instruct v2?
35 consumer GPUs can run Qwen3 4B Hindi Instruct v2 at Q4_K_M (2.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3 4B Hindi Instruct v2?
33 devices with unified memory can run Qwen3 4B Hindi Instruct v2 at Q4_K_M (2.9 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.