Sage A1.5 — Hardware Requirements & GPU Compatibility
ChatVisionCodeFunctions
Sage A1.5 is a 36.0B-parameter open language model from InternScience. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 21.95 GB of VRAM — see which GPUs and Macs can run it below.
Runs well on a 24 GB GPU
Runs from 16 GB (IQ3_S · 32K)Best on 24 GB (Q4_K_M · ~151 tok/s · 128K)Macs from 24 GB (IQ4_XS · ~50 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Sage A1.5
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/na0x2c6/Sage-A1.5-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 2.0 GB free · 16K context
Download: Sage-A1.5-Q4_K_M.gguf21.71 GBna0x2c6
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX A6000 (48 GB) (26 GB headroom) · Q4_K_M
- Generation speed
- ~125 tok/s
- generation speed
- Cost per 1M output tokens
- $0.73
- per 1M output tokens
How Much VRAM Does Sage A1.5 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 15.7 GB | 15.9 GB | 3-bit · i-quant small — Importance-weighted 3-bit, small | Fits: 256K context · ~187 tok/s | |
| 15.7 GB | 15.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~187 tok/s | |
| 17.9 GB | 18.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~172 tok/s | |
| 19.7 GB | 20 GB | 4-bit · i-quant compact — Importance-weighted 4-bit, compact | Fits: 256K context · ~162 tok/s | |
| 21.9 GB | 22.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 128K context · ~151 tok/s | |
| 26 GB | 26.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 26.1 GB | |
| 30.0 GB | 30.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 30.1 GB | |
| 36.3 GB | 36.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 36.4 GB | |
| 72.3 GB | 72.5 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 72.4 GB |
Selected Q4_K_M
4-bit medium quantization22.0GBat 16K context
- Weights
- 21.6 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 2.0 GB free · ~151 tok/s
≥50 tok/s needs ≥250 GB/s · RTX 5090 ~213, RTX 3090 Ti ~151
Or .
Which Hardware Can Run Sage A1.5?
Q4_K_M · 21.9 GBSage A1.5 (Q4_K_M) requires 21.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 29+ GB is recommended. Using the full 262K context window can add up to 2.6 GB, bringing total usage to 24.6 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090. 100 devices with unified memory can run Sage A1.5, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (36 GB).
20 GB
Q4_K_M · best fit IQ4_XS (19.8 GB)
1 GPU
RX 7900 XT~75Simulate output on RX 7900 XT32–40 GB
fits · 10.0 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~213Simulate output on RTX 5090M5 Max36 GB~59Simulate output on Mac Studio M5 Max (36 GB)M532 GB~29Simulate output on MacBook Air 13" M5 (32 GB)Orin~42Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~27Simulate 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 |
|---|---|---|---|---|
| Qwen3.6 35B A3BAlibaba | 106.8 | 22 GB | ~151 | same size |
| MiniCPM V 4.7 35B A3BOpenBMB | — | 22.1 GB | ~169 | same size |
| Ornith 1.5 35B A3Bornith-ai | — | 22 GB | ~151 | same size |
| Intern S2 PreviewInternLM | — | 73.2 GB | —needs > 24 GB | same size |
| EXAONE 4.5 33BLGAI-EXAONE | — | 23.9 GB | ~33 | same size |
| Qwen3.5 35B A3B BaseAlibaba | — | 22 GB | ~151 | same size |
Specifications
- Publisher
- InternScience
- Parameters
- 36.0B
- Active parameters
- 4.7B per token
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-10-15
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Sage A1.5 need?
Sage A1.5 requires 21.9 GB of VRAM at Q4_K_M, or 72.3 GB at BF16. Full 262K context adds up to 2.6 GB (24.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 36.0B × 4.8 bits ÷ 8 = 21.6 GB
KV Cache + Overhead ≈ 0.3 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 ≈ 2.9 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Sage A1.5?
Yes, at Q4_K_M (21.9 GB) or lower. Higher quantizations like Q5_K_M (26 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Sage A1.5?
For Sage A1.5, Q4_K_M (21.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (26 GB) provides better quality if you have the VRAM. The smallest option is IQ3_S at 15.7 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Sage A1.5 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Sage A1.5, the smallest at IQ4_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 21.9 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).
Can I run Sage A1.5 locally?
Yes — Sage A1.5 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Sage A1.5?
At Q4_K_M, Sage A1.5 can reach ~116 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~151 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 → 2.9 GB active ÷ (8000 × 0.65) = 0.557 ms, plus 40 layers × 0.055 ms = 2.200 ms, so 1000 ÷ 2.757 ms = ~363 tok/s
Estimated speed at Q4_K_M (21.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Sage A1.5?
At Q4_K_M, the download is about 21.57 GB. The full-precision BF16 version is 71.90 GB. The smallest option (IQ3_S) is 15.28 GB.
Which GPUs can run Sage A1.5?
7 consumer GPUs can run Sage A1.5 at Q4_K_M. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
Which devices can run Sage A1.5?
100 devices with unified memory can run Sage A1.5 at Q4_K_M (21.9 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.