Ingot 27B DFlash2 — Hardware Requirements & GPU Compatibility
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Ingot 27B DFlash2 is a 27B-parameter open language model from dendriteholdings. At Q4_K_M it needs about 17.82 GB of VRAM — see which GPUs and Macs can run it below.
Decent on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~37 tok/s · 16K)Macs from 24 GB (Q4_K_M · ~12 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Ingot 27B DFlash2
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
vllm serve dendriteholdings/Ingot-27B-DFlash2 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 5090 (32 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~65 tok/s
- generation speed
- Cost per 1M output tokens
- $2.11
- per 1M output tokens
How Much VRAM Does Ingot 27B DFlash2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 12.6 GB | 20.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~52 tok/s | |
| 14.5 GB | 22.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~45 tok/s | |
| 17.8 GB | 25.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~37 tok/s | |
| 21.2 GB | 28.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~31 tok/s | |
| 24.5 GB | 32.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 28.1 GB | |
| 29.7 GB | 37.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 33.3 GB | |
| 59.4 GB | 67.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 63 GB |
Selected Q4_K_M
4-bit medium quantization21.4GBat 16K context
- Weights
- 17.0 GB
- KV cache
- 4.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 2.6 GB free · ~37 tok/s
≥50 tok/s needs ≥1.37 TB/s · RTX 5090 ~65
Which Hardware Can Run Ingot 27B DFlash2?
Q4_K_M · 17.8 GBIngot 27B DFlash2 (Q4_K_M) requires 17.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Ingot 27B DFlash2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).
20 GB
tight · up to 8K ctx
1 GPU
RX 7900 XT~27Simulate output on RX 7900 XT32–40 GB
fits · 10.6 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~65Simulate output on RTX 5090M5 Max36 GB~18Simulate output on Mac Studio M5 Max (36 GB)M532 GB~6Simulate output on MacBook Air 13" M5 (32 GB)Orin~8Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~5Simulate 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 |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | same size |
| Qwen3.6 27BAlibaba | 111.2 | 17.9 GB | ~38 | same size |
| Qwen3.5 27BAlibaba | 103.8 | 17.9 GB | ~38 | same size |
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | same size |
| Gemma 2 27B ITGoogle | 72.7 | 19.5 GB | ~37 | same size |
| Gemma 4 26B A4BGoogle | — | 16.5 GB | ~171 | same size |
Frequently Asked Questions
How much VRAM does Ingot 27B DFlash2 need?
Ingot 27B DFlash2 requires 17.8 GB of VRAM at Q4_K_M, or 59.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 1.6 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.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Ingot 27B DFlash2?
Yes, at Q5_K_M (21.2 GB) or lower. Higher quantizations like Q6_K (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Ingot 27B DFlash2?
For Ingot 27B DFlash2, Q4_K_M (17.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (21.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Ingot 27B DFlash2 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Ingot 27B DFlash2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 17.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Ingot 27B DFlash2 locally?
Yes — Ingot 27B DFlash2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Ingot 27B DFlash2?
At Q4_K_M, Ingot 27B DFlash2 can reach ~269 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 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 ÷ 17.8 × 0.65 = ~292 tok/s
Estimated speed at Q4_K_M (17.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Ingot 27B DFlash2?
At Q4_K_M, the download is about 16.20 GB. The full-precision BF16 version is 54.00 GB. The smallest option (Q2_K) is 11.47 GB.
Which GPUs can run Ingot 27B DFlash2?
8 consumer GPUs can run Ingot 27B DFlash2 at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Ingot 27B DFlash2?
122 devices with unified memory can run Ingot 27B DFlash2 at Q4_K_M (17.8 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.