Calculate the TPS of the Xeon Phi 7120X on local AI models

Intel 16 GB GDDR5 352 GB/s June 2013

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

432 models it can run

679 models in our catalogue altogether

Largest model it holds

Nemotron 3-Nano-30B-A3B

31.6B · Q3_K_M · 46.0 tok/s

Fastest model

Gemma 3 QAT 1B

96.9 tok/s · 1B

Which AI models can run on a Xeon Phi 7120X?

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.

432 models match

Calculating
Quantisation Fit
96.9 tok/s

58–155 · low confidence

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

58–155 · low confidence

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

58–155 · low confidence

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

58–155 · low confidence

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

58–155 · low confidence

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

58–155 · low confidence

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

54–144 · low confidence

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

53–141 · low confidence

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

53–141 · low confidence

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

53–141 · low confidence

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

53–141 · low confidence

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

48–129 · low confidence

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

48–129 · low confidence

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

48–129 · low confidence

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

48–129 · low confidence

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

47–126 · low confidence

Llama 3.2 1B 1.2B Sep 2024 2.2 GB 131k tokens Q8_0 Comfortable
77.7 tok/s

47–124 · low confidence

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

45–119 · low confidence

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

45–119 · low confidence

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

45–119 · low confidence

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

45–119 · low confidence

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

45–119 · low confidence

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

45–119 · low confidence

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

45–119 · low confidence

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

45–119 · 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

Xeon Phi 7120X 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
16 GB
Memory bandwidth
352 GB/s
Memory type
GDDR5
Memory bus width
512 bit
Memory clock
1.38 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
Knights Corner
Architecture
Knights
Generation
Knights Corner(x100)
Foundry
Intel
Process size
22 nm
Transistors
5 billion
Transistor density
6,900 K/mm²
Die size
720 mm²
Released
17 June 2013

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
1.24 GHz
Boost clock
1.33 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
976
Texture mapping units
32

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.

Single precision (FP32)
2.6 TFLOPS
Texture rate
43 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)
300 W
Suggested power supply
700 W
Bus interface
PCIe 3.0 x16
Slot width
Dual-slot
Dimensions
248 mm

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.

OpenCL
1.2
Shader model
5.0

Listings

Where to buy a Xeon Phi 7120X

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

Why memory is the number that matters here

Memory

16 GB

Bandwidth

352 GB/s

Largest model

Nemotron 3-Nano-30B-A3B

16 GB of GDDR5 puts the Xeon Phi 7120X comfortably into small and mid-sized models, with roughly 14.4 GB usable once the driver overhead is taken out. The largest models are out of reach without splitting them.

The memory bus moves 352 GB/s across a 512-bit bus. That is the number that governs generation speed — arithmetic per byte read is small enough that the bus, not the cores, is what everything waits on.

Bandwidth is clock times bus width, and this card clocks its memory at 1.38 GHz. Both halves matter, and neither is visible in a gaming benchmark.

The practical ceiling is Nemotron 3-Nano-30B-A3B at 31.6B, held at Q3_K_M and running at roughly 46.0 tokens per second.

The chip and how it was built

The Xeon Phi 7120X is built on the Knights Corner graphics processor, using Intel's Knights architecture, as part of the Knights Corner(x100) generation.

The chip is manufactured by Intel, on a 22 nm process, with a die measuring 720 mm², holding 5 billion transistors. 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 June 2013, roughly 13 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

Clocks run from 1.24 GHz at base to 1.33 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

There are 976 shading units, 32 texture mapping 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

300 W

The Xeon Phi 7120X is rated at 300 W, with a 700 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 248 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 3.0 x16. 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 Xeon Phi 7120X

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 North Mini Code 30B · Q3_K_M · Jun 2026 48.4 tok/s
  2. 02 Qwen 3.6-27B 27B · Q3_K_M · Apr 2026 9.7 tok/s
  3. 03 Qwen3.5-27B 27B · Q3_K_M · Feb 2026 9.7 tok/s
  4. 04 Nemotron 3-Nano-30B-A3B 31.6B · Q3_K_M · Dec 2025 46.0 tok/s
  5. 05 Nomos 1 30B · Q3_K_M · Dec 2025 48.4 tok/s
  6. 06 C2S-Scale 27B · Q3_K_M · Oct 2025 9.7 tok/s
  7. 07 Gemma-SEA-LION-v4-27B-IT 27B · Q3_K_M · Aug 2025 9.7 tok/s
  8. 08 ERNIE-4.5-VL-28B-A3B 28B · Q3_K_M · Jun 2025 51.9 tok/s
  9. 09 Qwen3-30B-A3B 30B · Q3_K_M · Apr 2025 48.4 tok/s
  10. 10 Gemma 3 QAT 27B 27B · Q3_K_M · Apr 2025 9.7 tok/s

The fastest AI models on a Xeon Phi 7120X

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 96.9 tok/s
  2. 02 Gemma 3 1B 1B · Q8_0 · 1.8 GB 96.9 tok/s
  3. 03 LLama 3..2 Typhoon 2 1B 1B · Q8_0 · 1.8 GB 96.9 tok/s
  4. 04 OLMo-1B 1B · Q8_0 · 1.8 GB 96.9 tok/s
  5. 05 HGRN 1B (WT 103) 1B · Q8_0 · 1.8 GB 96.9 tok/s
  6. 06 Pythia-1b 1B · Q8_0 · 1.8 GB 96.9 tok/s
  7. 07 OpenELM-1.1B 1.1B · Q8_0 · 1.9 GB 89.7 tok/s
  8. 08 TinyLlama-1.1B (1T token checkpoint) 1.1B · Q8_0 · 1.9 GB 88.1 tok/s
  9. 09 TinyLlama-1.1B (3T token checkpoint) 1.1B · Q8_0 · 1.9 GB 88.1 tok/s
  10. 10 DeciCoder-1B 1.1B · Q8_0 · 1.9 GB 88.1 tok/s

Step by step

How to work out the tokens per second of a Xeon Phi 7120X

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

    All 432 models the Xeon Phi 7120X handles are already listed. The search box takes a name or a size such as 27b, which matches on parameter count.

  2. 02

    Set the context length you will actually use

    Set the context to your real working length. Short questions cost almost nothing; a long document can consume a large share of the card's 16 GB.

  3. 03

    Choose how far you will compress

    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

    The figures are calculated, not measured. 96.9 tok/s on Gemma 3 QAT 1B is the fastest result on this card, and like every row it carries a range that reflects how much the runtime matters.

  5. 05

    Check the headroom before you decide

    The fit column separates models that just fit from those with room to spare — worth checking against the card's 16 GB before settling on one.

  6. 06

    Cross-check against other hardware

    Following a model through to its own page lists all the hardware that can run it, so you can see where the Xeon Phi 7120X sits against the alternatives.

Answers

Xeon Phi 7120X — common questions

01

Can a Xeon Phi 7120X run a model that does not fit in its memory?

It can be split, with the overflow held in system memory — but that part drags the whole thing down, and none of the 16 GB figures on this page assume it.

02

Would two Xeon Phi 7120X cards be twice as fast?

Pairing Xeon Phi 7120X cards buys headroom rather than pace: 32 GB of combined memory, at roughly the same generation speed as one.

03

What AI models can a Xeon Phi 7120X run?

432 of the 679 open-weight language models we track fit on a Xeon Phi 7120X 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 Xeon Phi 7120X can run?

The largest model in our catalogue that fits on a Xeon Phi 7120X is Nemotron 3-Nano-30B-A3B at 31.6B parameters, compressed to Q3_K_M. It generates roughly 46.0 tokens per second and needs about 14.4 GB of the card's memory.

05

How many tokens per second does a Xeon Phi 7120X produce?

It depends on the model. On a Xeon Phi 7120X the fastest model we track is Gemma 3 QAT 1B at about 96.9 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

Can a Xeon Phi 7120X run a 7B model?

Yes. For example a Xeon Phi 7120X runs Multi-Token Prediction 7B at Q8_0, using about 7.9 GB of memory and generating around 14.5 tokens per second.

07

Can a Xeon Phi 7120X run a 13B model?

Yes. For example a Xeon Phi 7120X runs DeepSeekMoE-16B at Q6_K, using about 13.7 GB of memory and generating around 48.9 tokens per second.

08

Can a Xeon Phi 7120X run a 30B model?

Yes. For example a Xeon Phi 7120X runs ERNIE-4.5-VL-28B-A3B at Q3_K_M, using about 12.9 GB of memory and generating around 51.9 tokens per second.

09

How much memory does a Xeon Phi 7120X have?

A Xeon Phi 7120X has 16 GB of GDDR5 memory. Around a tenth of that is reserved by the inference runtime and the driver, leaving roughly 14.4 GB available for a model and its conversation.

10

What is the memory bandwidth of a Xeon Phi 7120X?

The Xeon Phi 7120X has 352 GB/s of memory bandwidth, across a 512-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.

11

What type of memory does a Xeon Phi 7120X use?

It uses GDDR5 clocked at 1.38 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.

12

Who makes the Xeon Phi 7120X?

The Xeon Phi 7120X is a Intel product, with the chip manufactured by Intel, on a 22 nm process.

13

When was the Xeon Phi 7120X released?

The Xeon Phi 7120X was released in June 2013.

14

How much power does a Xeon Phi 7120X use?

The Xeon Phi 7120X has a rated board power of 300 W, and a 700 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.

15

Does the Xeon Phi 7120X support CUDA?

No. CUDA is NVIDIA-only, and the Xeon Phi 7120X 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.

16

What bus interface does the Xeon Phi 7120X use?

It uses PCIe 3.0 x16. 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.

17

Is the Xeon Phi 7120X good for running local AI models?

Its memory covers small and mid-sized models, though the largest are out of reach though its bandwidth means generation will feel slow on larger models. In total it runs 432 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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