vcg-a40-2c-10g-4vram by Vultr
(All-cores)
(Single-core)
Specifications
Server Metadata
Vendor ID | vultr |
Name | vcg-a40-2c-10g-4vram |
Description | Cloud GPU (2 vCPUs, 10.0 GiB RAM, 180 GB NVMe, 0.0833xA40 48 GiB VRAM) |
Family | Cloud GPU |
Hw Virt | |
Average Time To Start | 80.67 |
Status | active |
Observed At | 2026-08-15T01:49:43.736663 |
Availability
| REGION / ID | SPOT | ONDEMAND |
|---|
Processor
vCPUs | 2 |
CPU Allocation | Dedicated |
CPU Cores | 1 |
CPU Speed | 2 GHz |
CPU Architecture | x86_64 |
CPU Manufacturer | Intel |
CPU L1D Cache | 32 KiB |
CPU L1D Cache Total | 64 KiB |
CPU L1I Cache | 32 KiB |
CPU L1I Cache Total | 64 KiB |
CPU L2 Cache | 4 MiB |
CPU L2 Cache Total | 4 MiB |
CPU L3 Cache | 16 MiB |
CPU L3 Cache Total | 16 MiB |
CPU Flags | fpu, vme, de, pse, tsc, msr, pae, mce, cx8, apic, sep, mtrr, pge, mca, cmov, pat, pse36, clflush, mmx, fxsr, sse, sse2, ht, syscall, nx, rdtscp, lm, constant_tsc, rep_good, nopl, xtopology, cpuid, tsc_known_freq, pni, pclmulqdq, ssse3, fma, cx16, pcid, sse4_1, sse4_2, x2apic, movbe, popcnt, tsc_deadline_timer, aes, xsave, avx, f16c, rdrand, hypervisor, lahf_lm, abm, cpuid_fault, pti, ssbd, ibrs, ibpb, fsgsbase, bmi1, avx2, smep, bmi2, erms, invpcid, xsaveopt, arat |
Ecpus | 2 |
Scalability | 200 |
System Resources and Accelerators
| MEMORY | |
|---|---|
Memory Amount | 10 GiB |
Memory Amount Actual | 10 GiB |
| GPU | |
|---|---|
GPU Count | 0.0833 |
GPU Memory Min | 4 GiB |
GPU Memory Total | 4 GiB |
GPU Manufacturer | NVIDIA |
GPU Family | Ampere |
GPU Model | A40 |
GPUs |
| STORAGE | |
|---|---|
Storage Size | 180 GB |
Storage Type | nvme ssd |
Storages |
| NETWORK | |
|---|---|
Inbound Traffic | 0 GB/month |
Outbound Traffic | 4096 GB/month |
IPv4 | 1 |
CPU and System Topology
Server Description
A fractional GPU instance combining dedicated Intel vCPUs and NVIDIA Ampere acceleration for cost-efficient, entry-level machine learning inference.
Vultr vcg-a40-2c-10g-4vram is a fractional GPU instance designed for entry-level accelerated workloads. It features 2 dedicated Intel x86_64 vCPUs running at 2.0 GHz, 10.0 GB of system memory, 180 GB of NVMe SSD storage, and a fractional NVIDIA Ampere A40 GPU with 4 GB of VRAM. Performance benchmarks indicate strong capabilities in LLM prompt processing, reaching 3480.67 tokens/sec on a small 135M model and 202.17 tokens/sec on a medium 7B model. However, general compute and memory bandwidth benchmarks are weak, with multi-core CPU performance and database operations scoring in the lower percentiles. This profile makes the instance a cost-efficient option for lightweight machine learning inference, model prototyping, and development, while it remains constrained for heavy database or multi-threaded processing tasks.
Economics
Performance
Memory Bandwidth
Compression
OpenSSL
Passmark CPU Scores
| BENCHMARK | SCORE |
|---|---|
Mark | 3651 |
Compression | 42481 |
Encryption | 1186 |
Extended Instructions | 2743 |
Floating Point Maths | 7578 |
Integer Maths | 9613 |
Physics | 531 |
Prime Numbers | 30 |
Single Threaded | 2269 |
String Sorting | 5577 |
Passmark Memory Scores
| BENCHMARK | SCORE |
|---|---|
Memory Mark | 1735 |
Database Operations | 957 |
Memory Latency | 61 |
Memory Read Cached | 22516 |
Memory Read Uncached | 10381 |
Memory Write | 9696 |
Stress-ng Raw Scores
Stress-ng Relative Multicore Performance
LLM Inference Speed for Prompt Processing
LLM Inference Speed for Text Generation
Static Web Server
Redis
Alternatives
Servers of the Same Family
| INSTANCE | vCPUs | MEMORY | GPUs |
|---|---|---|---|
| vcg-a40-1c-5g-2vram | 1 | 5 GiB | 1⁄24 |
| vcg-a16-2c-8g-2vram | 2 | 8 GiB | ⅛ |
| vcg-a16-2c-16g-4vram | 2 | 16 GiB | ¼ |
| vcg-a16-3c-32g-8vram | 3 | 32 GiB | ½ |
| vcg-a40-4c-20g-8vram | 4 | 20 GiB | ⅙ |
| vcg-a40-6c-30g-12vram | 6 | 30 GiB | ¼ |
| vcg-a40-8c-40g-16vram | 8 | 40 GiB | ⅓ |
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