Will It Run AI

Can Yi 34B Chat run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C43Usable
Estimated from fit model

Yi 34B Chat needs ~38.4 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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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) — 38.4 GB, 8.6 tok/s, Runs well
38.4 GB required108.8 GB available
35% VRAM used

Fit status

Runs well

Decode

8.6 tok/s

TTFT

22578 ms

Safe context

200K

Memory

38.4 GB / 108.8 GB

Memory breakdown

Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelsYi 34B Chat on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API. When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed. Key benefits of running locally: - Full privacy: your prompts never leave your machine - No per-token cost or rate limits - Works offline once the model is downloaded - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb: Model size Q4 (4-bit) Q8 (8-bit) FP16 7B params ~4.3 GB ~7.5 GB ~14 GB 13B params ~7.9 GB ~13.9 GB ~26 GB 70B params ~42.7 GB ~74.9 GB ~140 GB Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 8.6 tok/s decode · 22.6s TTFT (warm) · 21 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well8.6 tok/s12315 ms200K
CodingCRuns well8.6 tok/s22578 ms200K
Agentic CodingCRuns well8.6 tok/s32841 ms200K
ReasoningCRuns well8.6 tok/s26683 ms200K
RAGCRuns well8.6 tok/s41051 ms200K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowC42
Q3_K_S
3
16.7 GB
LowC42
NVFP4
4
19.0 GB
MediumC42
Q4_K_M
4
20.7 GB
MediumC43
Q5_K_M
5
24.5 GB
HighC43
Q6_K
6
27.9 GB
HighC44
Q8_0
8
36.4 GB
Very HighC46
F16Best for your GPU
16
69.7 GB
MaximumC49

Get started

Copy-paste commands to run Yi 34B Chat on your machine.

Run

lms load Yi-34B-Chat && lms server start

Upgrade-Optionen

Hardware, die Yi 34B Chat gut ausführt

NVIDIANVIDIA H200 141GBGünstige Wahl
141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 2355%.211.1 tok/s Dekodierung

Raises estimated decode speed by about 2355%.

Adds memory headroom for longer context windows and future model growth.

ca. $30,000 MSRP

NVIDIANVIDIA H200 PCIe 141GBBestes Preis-Leistungs-Verhältnis
141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 2355%.211.1 tok/s Dekodierung

Raises estimated decode speed by about 2355%.

Adds memory headroom for longer context windows and future model growth.

ca. $30,000 MSRP

NVIDIANVIDIA B200 180GBNVIDIA-Upgrade
180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 3991%.351.8 tok/s Dekodierung

Raises estimated decode speed by about 3991%.

Adds memory headroom for longer context windows and future model growth.

ca. $30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Yi 34B Chat?

Yes, NVIDIA DGX Spark 128GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 8.6 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 38.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 34B Chat?

The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 34B Chat run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Yi 34B Chat achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22578ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on NVIDIA DGX Spark 128GB receives a C grade with 8.6 tok/s and 200K context.

What context window can Yi 34B Chat use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Yi 34B Chat can safely use up to 200K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Yi 34B Chat?

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 Yi 34B Chat
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