Calculate the TPS of the Xe DG1 on local AI models

Intel 4 GB LPDDR4X 68 GB/s January 2020

Every model in our catalogue assessed against this card at the context length and minimum quality you choose. Speed is an estimate for a single request, calculated from this card's memory bandwidth and the size of each model once compressed.

Calculated for this card

97 models it can run

679 models in our catalogue altogether

Largest model it holds

DeciLM 6B

5.7B · Q3_K_M · 8.9 tok/s

Fastest model

Gemma 3 QAT 1B

18.8 tok/s · 1B

Which AI models can run on a Xe DG1?

Set the inputs, read the answer

More context means more memory for the conversation cache. Speed is for a fresh conversation and does not change with this setting.

Hides models that would only fit by being compressed below this point.

97 models match

Calculating
Quantisation Fit
18.8 tok/s

11–30 · low confidence

Gemma 3 1B 1B Mar 2025 1.8 GB 33k tokens Q8_0 Comfortable
18.8 tok/s

11–30 · low confidence

Gemma 3 QAT 1B 1B Apr 2025 1.8 GB 33k tokens Q8_0 Comfortable
18.8 tok/s

11–30 · low confidence

HGRN 1B (WT 103) ≈ 1B Nov 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
18.8 tok/s

11–30 · low confidence

LLama 3..2 Typhoon 2 1B ≈ 1B Dec 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
18.8 tok/s

11–30 · low confidence

OLMo-1B ≈ 1B Feb 2024 1.8 GB 131k tokens ? Q8_0 Comfortable
18.8 tok/s

11–30 · low confidence

Pythia-1b ≈ 1B Apr 2023 1.8 GB 131k tokens ? Q8_0 Comfortable
17.4 tok/s

10–28 · low confidence

OpenELM-1.1B ≈ 1.1B May 2024 1.9 GB 131k tokens ? Q8_0 Comfortable
17.1 tok/s

10–27 · low confidence

DeciCoder-1B ≈ 1.1B Aug 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
17.1 tok/s

10–27 · low confidence

SantaCoder ≈ 1.1B Jan 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
17.1 tok/s

10–27 · low confidence

TinyLlama-1.1B (1T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
17.1 tok/s

10–27 · low confidence

TinyLlama-1.1B (3T token checkpoint) ≈ 1.1B Oct 2023 1.9 GB 131k tokens ? Q8_0 Comfortable
15.7 tok/s

9–25 · low confidence

EXAONE 4.0 (1.2B) ≈ 1.2B Jul 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
15.7 tok/s

9–25 · low confidence

MinerU2.5 ≈ 1.2B Sep 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
15.7 tok/s

9–25 · low confidence

Pleias 1.0 1.2B ≈ 1.2B Dec 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
15.7 tok/s

9–25 · low confidence

Pleias-RAG-1B ≈ 1.2B Apr 2025 2.0 GB 131k tokens ? Q8_0 Comfortable
15.3 tok/s

9–24 · low confidence

Llama 3.2 1B 1.2B Sep 2024 2.2 GB 54k tokens Q8_0 Comfortable
15.1 tok/s

9–24 · low confidence

MiniCPM-1.2B ≈ 1.2B Jun 2024 2.0 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

DeepSeek Coder 1.3B ≈ 1.3B Jan 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

DeepSeek-VL-1.3B ≈ 1.3B Mar 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

DigiRL ≈ 1.3B Jun 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

GLA Transformer 1.3B ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

Janus 1.3B ≈ 1.3B Oct 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

Kosmos-2.5 ≈ 1.3B Aug 2024 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

Otter ≈ 1.3B May 2023 2.1 GB 131k tokens ? Q8_0 Comfortable
14.5 tok/s

9–23 · low confidence

Phi-1 ≈ 1.3B Oct 2023 2.1 GB 131k tokens ? Q8_0 Comfortable

Speeds are estimates for a single request — one conversation at a time — calculated from memory bandwidth, model size and quantisation. Real throughput varies with the inference runtime and its version. Figures published by hardware vendors measure many simultaneous requests and are much higher.

On record

Xe DG1 full specification

Everything on record for this board, ordered by how much it bears on running a language model rather than by how a spec sheet would list it. Memory comes first because it decides the outcome; the rest is context.

Memory

The two specifications that decide what this card can run and how quickly. Capacity sets which models fit; bandwidth sets how many tokens per second they produce once they do.

Memory size
4 GB
Memory bandwidth
68 GB/s
Memory type
LPDDR4X
Memory bus width
128 bit
Memory clock
2.13 GHz

The chip

Which processor is on the board and how it was manufactured. A smaller process size generally means more performance for the same power.

Graphics processor
DG1
Architecture
Generation 12.1
Generation
Xe Graphics
Foundry
Intel
Process size
10 nm
Die size
95 mm²
Released
1 January 2020

Clock speeds

How fast the processor runs. Worth far less here than on a gaming benchmark: generating text is limited by memory bandwidth, so a higher clock barely moves the result.

Base clock
900 MHz
Boost clock
1.55 GHz

Processing units

What the chip contains. These drive graphics performance and matter mainly for processing a long prompt rather than for producing the answer.

Shading units
640
Texture mapping units
40
Render output units
20
L2 cache
1 MB

Theoretical performance

Peak arithmetic rates published for the board. These are ceilings that no real workload reaches, and generating text reaches a small fraction of them because it is limited by memory rather than arithmetic.

Half precision (FP16)
4 TFLOPS
Single precision (FP32)
2 TFLOPS
Double precision (FP64)
496 GFLOPS
Pixel rate
31 GPixel/s
Texture rate
62 GTexel/s

The board

What it takes to physically install and power the card — the practical constraints that decide whether it fits the machine you already own.

Power draw (TDP)
30 W
Suggested power supply
200 W
Power connectors
None
Bus interface
PCIe 4.0 x8
Slot width
Dual-slot
Dimensions
164 mm × 22 mm
Display outputs
1x HDMI, 3x DisplayPort

Software support

Which graphics and compute interfaces the card supports. CUDA compute capability is the one that bears on inference: below 7.0 there are no tensor cores, and modern inference software falls back to slower code paths.

DirectX
12.1
OpenGL
4.6
Vulkan
1.4
OpenCL
3.0
Shader model
6.6

Listings

Where to buy a Xe DG1

No vendor is currently listing this card. Listings come from vendors who publish them here directly — browse the vendor directory to see who is selling what.

What the numbers mean

Memory: the specification that decides everything

Memory

4 GB

Bandwidth

68 GB/s

Largest model

DeciLM 6B

At 4 GB of LPDDR4X the Xe DG1 is limited to the smaller end of the catalogue. About 3.6 GB is actually available to a runtime, and a model has to fit entirely inside it before generating anything at all.

At 68 GB/s across a 128-bit bus, bandwidth is this card's real constraint. Every token requires reading the entire model out of memory, so a large model will feel slow here even when it fits.

The figure is the memory clock — 2.13 GHz here — multiplied by the bus width. It is why core counts predict generation speed so poorly.

The practical ceiling is DeciLM 6B at 5.7B, held at Q3_K_M and running at roughly 8.9 tokens per second.

The chip and how it was built

The Xe DG1 is built on the DG1 graphics processor, using Intel's Generation 12.1 architecture, as part of the Xe Graphics generation.

The chip is manufactured by Intel, on a 10 nm process, with a die measuring 95 mm². A smaller process generally means more performance for the same power, though for language models it matters far less than the memory subsystem.

It was released in January 2020, roughly 6 years ago. Inference software support tends to follow hardware by a year or two, so a card of this age generally has mature, well-optimised code paths available to it.

Compute throughput, and why it matters less than it looks

FP16

4 TFLOPS

FP64

496 GFLOPS

On paper the Xe DG1 reaches 4 TFLOPS at half precision and 2 TFLOPS at single precision. These are peak figures no real workload sustains, and generating text reaches only a small fraction of them — decoding is limited by memory rather than arithmetic, which is why a card can look enormously powerful here and still produce tokens at an ordinary rate.

Double-precision throughput is 496 GFLOPS. It has no bearing on running a language model — no inference runtime uses it — but it separates datacentre parts from consumer ones, since the latter deliberately restrict it.

Clocks run from 900 MHz at base to 1.55 GHz boosted. Worth far less here than on a gaming benchmark: raising the clock speeds up the arithmetic, and the arithmetic is not what generation is waiting on.

Cache and processing units

backed by 1 MB of L2. Cache absorbs a share of the memory traffic that would otherwise hit the main bus, which is the one place on this page where a number other than bandwidth quietly affects generation speed — a large L2 lets more of the working set stay close to the cores.

There are 640 shading units, 40 texture mapping units, and 20 render output units. These drive graphics workloads and contribute to prompt processing, but they sit idle for much of the time a model spends generating a reply.

Power, size and installation

Power draw

30 W

The Xe DG1 is rated at 30 W, with a 200 W power supply suggested for the whole system. Running a language model keeps a card busy in bursts rather than continuously — it draws hard while generating and idles between requests — so sustained draw over a working day is usually well below the rated figure.

The board occupies a dual-slot, measuring 164 mm long. Worth checking against the case and power supply already in the machine, since the largest cards need considerably more of both than a typical desktop provides.

It connects over PCIe 4.0 x8. The interface governs how quickly a model is loaded from disk into the card, not how fast it runs once there, so a narrower link costs a few seconds at startup and nothing thereafter.

The extremes

The largest AI models that run on a Xe DG1

The biggest open-weight models that fit on this card, newest first. Each is shown at the best compression the card can hold.

  1. 01 Qwen3.5-4B 4B · Q5_K_M · Feb 2026 8.4 tok/s
  2. 02 Voxtral Mini 4.7B · Q4_K_M · Jul 2025 9.2 tok/s
  3. 03 Typhoon 2.1 Gemma 4B 4B · Q4_K_M · May 2025 10.9 tok/s
  4. 04 Qwen3-4B 4B · Q3_K_M · Apr 2025 12.7 tok/s
  5. 05 Gemma 3 QAT 4B 4B · Q4_K_M · Apr 2025 10.9 tok/s
  6. 06 Gemma 3 4B 4B · Q4_K_M · Mar 2025 10.9 tok/s
  7. 07 Phi-4-Multimodal 5.6B · Q3_K_M · Mar 2025 9.1 tok/s
  8. 08 Minitron 4B 4.2B · Q4_K_M · Nov 2024 10.3 tok/s
  9. 09 XVERSE-MoE-A4.2B 4.2B · Q4_K_M · Apr 2024 10.3 tok/s
  10. 10 DeciLM 6B 5.7B · Q3_K_M · Sep 2023 8.9 tok/s

The fastest AI models on a Xe DG1

Where this card produces tokens quickest. Smaller models dominate here, because generating each token means reading the whole model out of memory once.

  1. 01 Gemma 3 QAT 1B 1B · Q8_0 · 1.8 GB 18.8 tok/s
  2. 02 Gemma 3 1B 1B · Q8_0 · 1.8 GB 18.8 tok/s
  3. 03 LLama 3..2 Typhoon 2 1B 1B · Q8_0 · 1.8 GB 18.8 tok/s
  4. 04 OLMo-1B 1B · Q8_0 · 1.8 GB 18.8 tok/s
  5. 05 HGRN 1B (WT 103) 1B · Q8_0 · 1.8 GB 18.8 tok/s
  6. 06 Pythia-1b 1B · Q8_0 · 1.8 GB 18.8 tok/s
  7. 07 OpenELM-1.1B 1.1B · Q8_0 · 1.9 GB 17.4 tok/s
  8. 08 TinyLlama-1.1B (1T token checkpoint) 1.1B · Q8_0 · 1.9 GB 17.1 tok/s
  9. 09 TinyLlama-1.1B (3T token checkpoint) 1.1B · Q8_0 · 1.9 GB 17.1 tok/s
  10. 10 DeciCoder-1B 1.1B · Q8_0 · 1.9 GB 17.1 tok/s

Step by step

How to work out the tokens per second of a Xe DG1

You do not have to calculate anything by hand — the gputps.com calculator on this page has already worked it out for every model this card can hold. Reading off the answer takes six steps.

  1. 01

    Find the model in the table

    Every one of the 97 models this Xe DG1 runs is in the table above. Search narrows it by name or by size.

  2. 02

    Decide how long your conversations run

    Drag the slider to the conversation length you plan to work at. The cache grows with the conversation, and on 4 GB it is often what pushes a large model over the edge.

  3. 03

    Set a minimum quality if you need one

    By default the table picks the least-compressed copy that fits. Setting a floor removes models that only qualify through heavy compression.

  4. 04

    Take the range as the answer

    Speeds come with error bars for a reason. The best case here is 18.8 tok/s on Gemma 3 QAT 1B, and which inference software you use moves that by thirty to fifty per cent.

  5. 05

    Read the fit verdict last

    The fit column separates models that just fit from those with room to spare — worth checking against the card's 4 GB before settling on one.

  6. 06

    Check the same model from the other side

    Each model page repeats this calculation for the whole catalogue. Worth a look before deciding: it shows what else runs the same model, and how the Xe DG1 compares.

Answers

Xe DG1 — common questions

01

Can a Xe DG1 run a model that does not fit in its memory?

Only partly. Layers beyond the 4 GB sit in system memory and run at a fraction of the speed, so a mostly-offloaded model is rarely worth using. Every figure here assumes it is fully resident on the card.

02

Would two Xe DG1 cards be twice as fast?

Capacity adds, throughput does not. Two of them give you 8 GB to work with rather than twice the tokens per second — every figure here is for a single Xe DG1.

03

What AI models can a Xe DG1 run?

97 of the 679 open-weight language models we track fit on a Xe DG1 and can be run locally on it. The table on this page lists every one, with the memory it needs, the quantisation it runs at and an estimated generation speed.

04

What is the largest AI model a Xe DG1 can run?

The largest model in our catalogue that fits on a Xe DG1 is DeciLM 6B at 5.7B parameters, compressed to Q3_K_M. It generates roughly 8.9 tokens per second and needs about 3.5 GB of the card's memory.

05

How many tokens per second does a Xe DG1 produce?

It depends on the model. On a Xe DG1 the fastest model we track is Gemma 3 QAT 1B at about 18.8 tokens per second, while larger models run proportionally slower because each token requires reading the whole model out of memory once. Speeds are estimates for a single conversation at a time.

06

How much memory does a Xe DG1 have?

A Xe DG1 has 4 GB of LPDDR4X memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 3.6 GB available for a model and its conversation.

07

What is the memory bandwidth of a Xe DG1?

The Xe DG1 has 68 GB/s of memory bandwidth, across a 128-bit memory bus. This is the single best predictor of how fast it generates text, because producing each token means reading the entire model out of memory once.

08

What type of memory does a Xe DG1 use?

It uses LPDDR4X clocked at 2.13 GHz. HBM types are found on datacentre accelerators and carry far more bandwidth than the GDDR used on desktop cards, which is why they generate tokens considerably faster at the same capacity.

09

Who makes the Xe DG1?

The Xe DG1 is a Intel product, with the chip manufactured by Intel, on a 10 nm process.

10

When was the Xe DG1 released?

The Xe DG1 was released in January 2020.

11

How much power does a Xe DG1 use?

The Xe DG1 has a rated board power of 30 W, and a 200 W system power supply is suggested. Generating text draws hard in bursts and idles between requests, so average consumption over a working session is normally well below the rated figure.

12

How much cache does a Xe DG1 have?

and 1 MB of L2 cache. Cache absorbs part of the memory traffic that would otherwise reach the main bus, so a larger L2 gives a modest lift to generation speed beyond what bandwidth alone predicts.

13

What are the TFLOPS of a Xe DG1?

The Xe DG1 is rated at 4 TFLOPS at half precision and 2 TFLOPS at single precision. These are peak arithmetic ceilings rather than achievable rates, and text generation reaches only a small fraction of them because it is limited by memory bandwidth instead.

14

Does the Xe DG1 support CUDA?

No. CUDA is NVIDIA-only, and the Xe DG1 is a Intel card. It runs language models through ROCm, Vulkan or Metal depending on the software, which are less mature than the CUDA path — our estimates apply a penalty for that.

15

What bus interface does the Xe DG1 use?

It uses PCIe 4.0 x8. This governs how fast a model is loaded onto the card rather than how fast it runs once loaded, so it costs a few seconds at startup and nothing during generation.

16

Is the Xe DG1 good for running local AI models?

Its memory limits it to smaller models though its bandwidth means generation will feel slow on larger models. In total it runs 97 of the models we track. Whether that is enough depends entirely on which model you want — the table above answers that directly.

The other direction

Looking at it from the other side?

This page starts from the hardware. If you already know which model you want and need to know what it takes to run it, start from the model instead.

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