Instella MoE 16B A3B Think — Hardware Requirements & GPU Compatibility
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Instella MoE 16B A3B Think is a 15.9B-parameter open language model from amd. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 10.27 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~234 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~54 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Instella MoE 16B A3B Think
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve amd/Instella-MoE-16B-A3B-Think --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3090 (24 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~223 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does Instella MoE 16B A3B Think Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 7.5 GB | 14.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~285 tok/s | |
| 8.5 GB | 15.3 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~264 tok/s | |
| 10.3 GB | 17.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~234 tok/s | |
| 12.1 GB | 18.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~210 tok/s | |
| 13.8 GB | 20.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~191 tok/s | |
| 16.6 GB | 23.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~167 tok/s | |
| 32.5 GB | 39.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 35.7 GB |
Selected Q4_K_M
4-bit medium quantization13.4GBat 16K context
- Weights
- 9.5 GB
- KV cache
- 3.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 10.6 GB free · ~234 tok/s
≥50 tok/s needs ≥151 GB/s · RTX 5090 ~328, RTX 3090 Ti ~234
Which Hardware Can Run Instella MoE 16B A3B Think?
Q4_K_M · 10.3 GBInstella MoE 16B A3B Think (Q4_K_M) requires 10.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 33K context window can add up to 6.8 GB, bringing total usage to 17.1 GB. 38 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti. 150 devices with unified memory can run Instella MoE 16B A3B Think, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M2 Pro (16 GB).
16–18 GB
16–18 GB tier →fits · 2.6 GB free · up to 16K ctx
18 GPUs · 2 Mac classes · 2 device families
RTX 5080~227Simulate output on RTX 5080RTX 5070 Ti~217Simulate output on RTX 5070 TiRTX 4080 SUPER~189Simulate output on RTX 4080 SUPERM516 GB~45Simulate output on MacBook Air 13" M5 (16 GB)M3 Pro*18 GB~44Simulate output on MacBook Pro 14" M3 Pro (18 GB)iPad~37Simulate output on iPad Pro M4 13" (16 GB)Orin NX~35Simulate output on NVIDIA Jetson Orin NX 16GB20 GB
fits · 6.6 GB free · up to 32K ctx
1 GPU
RX 7900 XT~109Simulate output on RX 7900 XT32–40 GB
fits · 18.6 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~328Simulate output on RTX 5090M5 Max36 GB~90Simulate output on Mac Studio M5 Max (36 GB)M532 GB~45Simulate output on MacBook Air 13" M5 (32 GB)Orin~66Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~43Simulate 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 |
|---|---|---|---|---|
| Kimi VL A3B InstructMoonshot AI | — | 10.7 GB | ~208 | same size |
| Nemotron Terminal 14BNVIDIA | — | 11.9 GB | ~69 | same size |
| Qwen3 14B BaseAlibaba | — | 11.9 GB | ~69 | same size |
| Moonlight 16B A3BMoonshot AI | — | 10.1 GB | ~229 | same size |
| DeepSeek R1 Distill Qwen 14BDeepSeek | — | 12.4 GB | ~69 | same size |
| Qwen2.5 14B InstructAlibaba | — | 12.4 GB | ~69 | same size |
Specifications
- Publisher
- amd
- Parameters
- 15.9B
- Active parameters
- 2.8B per token
- Architecture
- InstellaMoEForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 128,896
- Release Date
- 2026-07-23
- License
- Other
Frequently Asked Questions
How much VRAM does Instella MoE 16B A3B Think need?
Instella MoE 16B A3B Think requires 10.3 GB of VRAM at Q4_K_M, or 32.5 GB at BF16. Full 33K context adds up to 6.8 GB (17.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 15.9B × 4.8 bits ÷ 8 = 9.5 GB
KV Cache + Overhead ≈ 0.8 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 ≈ 7.6 GB (at full 33K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Instella MoE 16B A3B Think?
Yes, at Q8_0 (16.6 GB) or lower. Higher quantizations like BF16 (32.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Instella MoE 16B A3B Think?
For Instella MoE 16B A3B Think, Q4_K_M (10.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (12.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Instella MoE 16B A3B Think on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Instella MoE 16B A3B Think. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 10.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Instella MoE 16B A3B Think locally?
Yes — Instella MoE 16B A3B Think can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Instella MoE 16B A3B Think?
At Q4_K_M, Instella MoE 16B A3B Think can reach ~173 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~234 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.8 GB active ÷ (8000 × 0.65) = 0.351 ms, plus 27 layers × 0.055 ms = 1.485 ms, so 1000 ÷ 1.836 ms = ~545 tok/s
Estimated speed at Q4_K_M (10.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Instella MoE 16B A3B Think?
At Q4_K_M, the download is about 9.52 GB. The full-precision BF16 version is 31.73 GB. The smallest option (Q2_K) is 6.74 GB.
Which GPUs can run Instella MoE 16B A3B Think?
38 consumer GPUs can run Instella MoE 16B A3B Think at Q4_K_M. Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
Which devices can run Instella MoE 16B A3B Think?
150 devices with unified memory can run Instella MoE 16B A3B Think at Q4_K_M (10.3 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.