Will It Run AI

Can DeepSeek R1 Distill 70B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

A71Great
Estimated from fit model

DeepSeek R1 Distill 70B needs ~61.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~4 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth
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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) — 61.8 GB, 4.2 tok/s, Runs well
61.8 GB required108.8 GB available
57% VRAM used

Fit status

Runs well

Decode

4.2 tok/s

TTFT

46408 ms

Safe context

131K

Memory

61.8 GB / 108.8 GB

Memory breakdown

Weights42.7 GB
KV Cache4.9 GB
Runtime1.2 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 70B on NVIDIA DGX Spark 128GB
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: 4.2 tok/s decode · 46.4s TTFT (warm) · 10 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatARuns well4.2 tok/s25313 ms131K
CodingARuns well4.2 tok/s46408 ms131K
Agentic CodingARuns well4.2 tok/s67502 ms131K
ReasoningARuns well4.2 tok/s54845 ms131K
RAGARuns well4.2 tok/s84378 ms131K

Quantization options

How DeepSeek R1 Distill 70B (70B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowB69
Q3_K_S
3
34.3 GB
LowA70
NVFP4
4
39.2 GB
MediumA72
Q4_K_M
4
42.7 GB
MediumA72
Q5_K_M
5
50.4 GB
HighA74
Q6_K
6
57.4 GB
HighA74
Q8_0Best for your GPU
8
74.9 GB
Very HighA74
F16
16
143.5 GB
MaximumF0

Get started

Copy-paste commands to run DeepSeek R1 Distill 70B on your machine.

Run

ollama run deepseek-r1:70b

Your hardware

More models your NVIDIA DGX Spark 128GB can run

ModelParamsGradeDecodeCapabilities
MistralDevstral 2 123B Instruct123BS2.4 tok/s
AlibabaQwen 3.5 122B A10B122BS6.6 tok/s
MistralMistral Small 4 119B119BS7.1 tok/s
OpenAIGPT-OSS 120B117BA2.5 tok/s
CohereCommand A 111B111BS2.6 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run DeepSeek R1 Distill 70B?

Yes, NVIDIA DGX Spark 128GB can run DeepSeek R1 Distill 70B with a A grade (Runs well). Expected decode speed: 4.2 tok/s.

How much VRAM does DeepSeek R1 Distill 70B need?

DeepSeek R1 Distill 70B (70B parameters) requires approximately 61.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 70B?

The recommended quantization for DeepSeek R1 Distill 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 70B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, DeepSeek R1 Distill 70B achieves approximately 4.2 tokens per second decode speed with a time-to-first-token of 46408ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run DeepSeek R1 Distill 70B for coding?

For coding workloads, DeepSeek R1 Distill 70B on NVIDIA DGX Spark 128GB receives a A grade with 4.2 tok/s and 131K context.

What context window can DeepSeek R1 Distill 70B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, DeepSeek R1 Distill 70B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if DeepSeek R1 Distill 70B feels slow on NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for DeepSeek R1 Distill 70B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GBSee all hardware for DeepSeek R1 Distill 70B
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