Can CodeGeeX 4 9B run on RTX 2070 Super 8GB?

Q: What should I upgrade first if CodeGeeX 4 9B feels slow on RTX 2070 Super 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

A80Great

Estimated from fit model

CodeGeeX 4 9B needs ~7.8 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.8 GB, 54.4 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

54.4 tok/s

TTFT

3556 ms

Safe context

21K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsCodeGeeX 4 9B on RTX 2070 Super 8GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 54.4 tok/s decode · 3.6s TTFT (warm) · 136 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	54.4 tok/s	1940 ms	21K
Coding	A	Runs with offload	54.4 tok/s	3556 ms	21K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	35.6 tok/s	7915 ms	21K
Reasoning	A	Runs with offload	54.4 tok/s	4202 ms	21K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	35.6 tok/s	9894 ms

Quantization options

How CodeGeeX 4 9B (9B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A81
Q3_K_S	3	4.4 GB	Low	A81
NVFP4Best for your GPU

Get started

Copy-paste commands to run CodeGeeX 4 9B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "THUDM/codegeex4-all-9b" \
  --hf-file "codegeex4-all-9b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 2070 Super 8GB run CodeGeeX 4 9B?

Yes, RTX 2070 Super 8GB can run CodeGeeX 4 9B with a A grade (Runs with offload). Expected decode speed: 54.4 tok/s.

How much VRAM does CodeGeeX 4 9B need?

CodeGeeX 4 9B (9B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeGeeX 4 9B?

The recommended quantization for CodeGeeX 4 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeGeeX 4 9B run at on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, CodeGeeX 4 9B achieves approximately 54.4 tokens per second decode speed with a time-to-first-token of 3556ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run CodeGeeX 4 9B for coding?

For coding workloads, CodeGeeX 4 9B on RTX 2070 Super 8GB receives a A grade with 54.4 tok/s and 21K context.

What context window can CodeGeeX 4 9B use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, CodeGeeX 4 9B can safely use up to 21K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if CodeGeeX 4 9B feels slow on RTX 2070 Super 8GB?

See all results for RTX 2070 Super 8GB See all hardware for CodeGeeX 4 9B

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