Updated minutes ago
WizardLM

WizardCoder 33B

WizardLM · dense · 33B parameters · 16,384 context

Quality
50.0
Calculate ROI →Compare with othersFine-Tune This Model →

Parameters

33B

Context Window

16K tokens

Architecture

Dense

Best GPU

H20

Intelligence Brief

WizardCoder 33B is a 33B parameter DENSE model from WizardLM, featuring Grouped Query Attention (GQA) with 62 layers and 7,168 hidden dimensions. With a 16,384 token context window, it supports code, math. For self-hosted inference, H20 delivers optimal throughput at $940/month.

Architecture Details

TypeDENSE
Total Parameters33B
Active Parameters33B
Layers62
Hidden Dimension7,168
Attention Heads56
KV Heads8
Head Dimension128
Vocab Size32,256

Memory Requirements

BF16 Weights

66.0 GB

FP8 Weights

33.0 GB

INT4 Weights

16.5 GB

KV-Cache per Token253952 bytes
Activation Estimate1.80 GB

Fits on (single GPU) — most practical first

BEST FITA10G24GBINT4A3024GBINT4L424GBINT4RTX 409024GBINT4RTX 309024GBINT4RTX A500024GBINT4RX 7900 XTX24GBINT4RTX 509032GBINT4V100 32GB32GBINT4Instinct MI10032GBINT4TPU v432GBINT4TPU v6e (Trillium)32GBINT4

GPU Compatibility Matrix

WizardCoder 33B is compatible with 57% of GPU configurations across 41 GPUs at 3 precision levels.

BF16 (Full)
FP8 (Half)
INT4 (Quarter)
Blackwell(7 GPUs)
B200 NVL (pair)360GB
82%
91%
95%
B300288GB
77%
89%
94%
B100 SXM192GB
66%
83%
91%
GB200 NVL72 (per GPU)192GB
66%
83%
91%
Hopper(7 GPUs)
H100 NVL 94GB (per GPU pair)188GB
65%
82%
91%
H200 SXM141GB
53%
77%
88%
H2096GB
31%
66%
83%
GH20096GB
31%
66%
83%
Ada Lovelace(11 GPUs)
L40S48GB
✗
31%
66%
L4048GB
✗
31%
66%
RTX 6000 Ada48GB
✗
31%
66%
L2048GB
✗
31%
66%
Ampere(16 GPUs)
A100 80GB SXM80GB
18%
59%
79%
A100 80GB PCIe80GB
18%
59%
79%
A1664GB
✗
48%
74%
RTX A600048GB
✗
31%
66%
Legend:No fitVery tightTightModerateGoodExcellent

GPU Recommendations

H20optimal

FP8 · 1 GPU · tensorrt-llm

100/100

score

Throughput

1.0K tok/s

Latency (ITL)

1.0ms

Est. TTFT

0ms

Cost/Month

$940

Cost/M Tokens

$0.35

Use this config →
H200 SXMoptimal

FP8 · 1 GPU · tensorrt-llm

95/100

score

Throughput

1.1K tok/s

Latency (ITL)

1.0ms

Est. TTFT

0ms

Cost/Month

$2553

Cost/M Tokens

$0.93

Use this config →
H100 SXMoptimal

FP8 · 1 GPU · tensorrt-llm

95/100

score

Throughput

849.3 tok/s

Latency (ITL)

1.2ms

Est. TTFT

0ms

Cost/Month

$1794

Cost/M Tokens

$0.80

Use this config →

Deployment Options

API

API Deployment

No API pricing available

Self-Hosted

Single GPU

H20

$940/mo

Min VRAM: 33 GB

Scale

Multi-GPU

RTX A6000 x2

110.6 tok/s

TP· $930/mo

Open Full Calculator →

API Pricing Comparison

No API pricing data available for this model.

Performance Estimates

Throughput by GPU

H20
1.0K tok/s
H200 SXM
1.1K tok/s
H100 SXM
849.3 tok/s

VRAM Breakdown (H20, FP8)

Weights
Act
Weights 33.0 GBKV-Cache 2.1 GBActivations 14.4 GBOverhead 1.7 GB

Precision Impact

bf16

66.0 GB

weights/GPU

fp8

33.0 GB

weights/GPU

~1.0K tok/s

int4

16.5 GB

weights/GPU

Capabilities

Features

✗ Tool Use✗ Vision✓ Code✓ Math✗ Reasoning✗ Multilingual✗ Structured Output

Supported Frameworks

vllmsglangtgitensorrt-llm

Supported Precisions

BF16 (default)FP8INT4

Where to Deploy WizardCoder 33B

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Frequently Asked Questions

How much VRAM does WizardCoder 33B need for inference?

WizardCoder 33B requires approximately 66.0 GB of VRAM at BF16 precision, 33.0 GB at FP8, or 16.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (253952 bytes per token) and activations (~1.80 GB).

What is the best GPU for WizardCoder 33B?

The top recommended GPU for WizardCoder 33B is the H20 using FP8 precision. It achieves approximately 1.0K tokens/sec at an estimated cost of $940/month ($0.35/M tokens). Score: 100/100.

How much does WizardCoder 33B inference cost?

WizardCoder 33B inference costs vary by provider and GPU setup. Use our calculator for detailed cost estimates across all providers.